most_recent_publications_2017-04-19.xml

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    <MedlineCitation Status="MEDLINE" Owner="NLM">
        <PMID Version="1">27801980</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>11</Month>
            <Day>01</Day>
        </DateCreated>
        <DateCompleted>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateCompleted>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Print-Electronic">
            <Journal>
                <ISSN IssnType="Electronic">1755-0998</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>17</Volume>
                    <Issue>1</Issue>
                    <PubDate>
                        <Year>2017</Year>
                        <Month>Jan</Month>
                    </PubDate>
                </JournalIssue>
                <Title>Molecular ecology resources</Title>
                <ISOAbbreviation>Mol Ecol Resour</ISOAbbreviation>
            </Journal>
            <ArticleTitle>phylodyn: an R package for phylodynamic simulation and inference.</ArticleTitle>
            <Pagination>
                <MedlinePgn>96-100</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1111/1755-0998.12630</ELocationID>
            <Abstract>
                <AbstractText>We introduce phylodyn, an r package for phylodynamic analysis based on gene genealogies. The package's main functionality is Bayesian nonparametric estimation of effective population size fluctuations over time. Our implementation includes several Markov chain Monte Carlo-based methods and an integrated nested Laplace approximation-based approach for phylodynamic inference that have been developed in recent years. Genealogical data describe the timed ancestral relationships of individuals sampled from a population of interest. Here, individuals are assumed to be sampled at the same point in time (isochronous sampling) or at different points in time (heterochronous sampling); in addition, sampling events can be modelled with preferential sampling, which means that the intensity of sampling events is allowed to depend on the effective population size trajectory. We assume the coalescent and the sequentially Markov coalescent processes as generative models of genealogies. We include several coalescent simulation functions that are useful for testing our phylodynamics methods via simulation studies. We compare the performance and outputs of various methods implemented in phylodyn and outline their strengths and weaknesses. r package phylodyn is available at https://github.com/mdkarcher/phylodyn.</AbstractText>
                <CopyrightInformation>© 2016 John Wiley &amp; Sons Ltd.</CopyrightInformation>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Karcher</LastName>
                    <ForeName>Michael D</ForeName>
                    <Initials>MD</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Statistics, University of Washington, Seattle, WA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Palacios</LastName>
                    <ForeName>Julia A</ForeName>
                    <Initials>JA</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Statistics, Stanford University, Stanford, CA, USA.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Lan</LastName>
                    <ForeName>Shiwei</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Statistics, University of Warwick, Coventry, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Minin</LastName>
                    <ForeName>Vladimir N</ForeName>
                    <Initials>VN</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Statistics, University of Washington, Seattle, WA, USA.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Biology, University of Washington, Seattle, WA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>R01 AI107034</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U54 GM111274</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>21</Day>
            </ArticleDate>
        </Article>
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            <Country>England</Country>
            <MedlineTA>Mol Ecol Resour</MedlineTA>
            <NlmUniqueID>101465604</NlmUniqueID>
            <ISSNLinking>1755-098X</ISSNLinking>
        </MedlineJournalInfo>
        <CitationSubset>IM</CitationSubset>
        <MeshHeadingList>
            <MeshHeading>
                <DescriptorName UI="D056808" MajorTopicYN="N">Biostatistics</DescriptorName>
                <QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName>
                <QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D003198" MajorTopicYN="Y">Computer Simulation</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D005828" MajorTopicYN="N">Genetics, Population</DescriptorName>
                <QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D011157" MajorTopicYN="Y">Population Dynamics</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D012984" MajorTopicYN="Y">Software</DescriptorName>
            </MeshHeading>
        </MeshHeadingList>
        <KeywordList Owner="NOTNLM">
            <Keyword MajorTopicYN="Y">Inla</Keyword>
            <Keyword MajorTopicYN="Y">Mcmc</Keyword>
            <Keyword MajorTopicYN="Y">evolutionary theory</Keyword>
            <Keyword MajorTopicYN="Y">population dynamics</Keyword>
            <Keyword MajorTopicYN="Y">population genetics - theoretical</Keyword>
        </KeywordList>
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            <PubMedPubDate PubStatus="received">
                <Year>2016</Year>
                <Month>06</Month>
                <Day>10</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="revised">
                <Year>2016</Year>
                <Month>10</Month>
                <Day>18</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2016</Year>
                <Month>10</Month>
                <Day>20</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>2</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="medline">
                <Year>2017</Year>
                <Month>4</Month>
                <Day>4</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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            <PubMedPubDate PubStatus="entrez">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>2</Day>
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            <ArticleId IdType="pubmed">27801980</ArticleId>
            <ArticleId IdType="doi">10.1111/1755-0998.12630</ArticleId>
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<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">28053012</PMID>
        <DateCreated>
            <Year>2017</Year>
            <Month>01</Month>
            <Day>05</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Print-Electronic">
            <Journal>
                <ISSN IssnType="Electronic">1537-1719</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>34</Volume>
                    <Issue>1</Issue>
                    <PubDate>
                        <Year>2017</Year>
                        <Month>Jan</Month>
                    </PubDate>
                </JournalIssue>
                <Title>Molecular biology and evolution</Title>
                <ISOAbbreviation>Mol. Biol. Evol.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Phylogenetic Tools for Generalized HIV-1 Epidemics: Findings from the PANGEA-HIV Methods Comparison.</ArticleTitle>
            <Pagination>
                <MedlinePgn>185-203</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1093/molbev/msw217</ELocationID>
            <Abstract>
                <AbstractText>Viral phylogenetic methods contribute to understanding how HIV spreads in populations, and thereby help guide the design of prevention interventions. So far, most analyses have been applied to well-sampled concentrated HIV-1 epidemics in wealthy countries. To direct the use of phylogenetic tools to where the impact of HIV-1 is greatest, the Phylogenetics And Networks for Generalized HIV Epidemics in Africa (PANGEA-HIV) consortium generates full-genome viral sequences from across sub-Saharan Africa. Analyzing these data presents new challenges, since epidemics are principally driven by heterosexual transmission and a smaller fraction of cases is sampled. Here, we show that viral phylogenetic tools can be adapted and used to estimate epidemiological quantities of central importance to HIV-1 prevention in sub-Saharan Africa. We used a community-wide methods comparison exercise on simulated data, where participants were blinded to the true dynamics they were inferring. Two distinct simulations captured generalized HIV-1 epidemics, before and after a large community-level intervention that reduced infection levels. Five research groups participated. Structured coalescent modeling approaches were most successful: phylogenetic estimates of HIV-1 incidence, incidence reductions, and the proportion of transmissions from individuals in their first 3 months of infection correlated with the true values (Pearson correlation &gt; 90%), with small bias. However, on some simulations, true values were markedly outside reported confidence or credibility intervals. The blinded comparison revealed current limits and strengths in using HIV phylogenetics in challenging settings, provided benchmarks for future methods' development, and supports using the latest generation of phylogenetic tools to advance HIV surveillance and prevention.</AbstractText>
                <CopyrightInformation>© The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.</CopyrightInformation>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Ratmann</LastName>
                    <ForeName>Oliver</ForeName>
                    <Initials>O</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analyses and Modelling, School of Public Health, Imperial College London, London, United Kingdom oliver.ratmann@imperial.ac.uk.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Hodcroft</LastName>
                    <ForeName>Emma B</ForeName>
                    <Initials>EB</Initials>
                    <AffiliationInfo>
                        <Affiliation>School of Biological Sciences, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Pickles</LastName>
                    <ForeName>Michael</ForeName>
                    <Initials>M</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analyses and Modelling, School of Public Health, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cori</LastName>
                    <ForeName>Anne</ForeName>
                    <Initials>A</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analyses and Modelling, School of Public Health, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Hall</LastName>
                    <ForeName>Matthew</ForeName>
                    <Initials>M</Initials>
                    <AffiliationInfo>
                        <Affiliation>School of Biological Sciences, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Nuffield Department of Medicine, Li Ka Shing Centre for Health Information and Discovery, Oxford Big Data Institute, University of Oxford, Oxford, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Lycett</LastName>
                    <ForeName>Samantha</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>School of Biological Sciences, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Colijn</LastName>
                    <ForeName>Caroline</ForeName>
                    <Initials>C</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Mathematics, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Dearlove</LastName>
                    <ForeName>Bethany</ForeName>
                    <Initials>B</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Veterinary Medicine, Cambridge Veterinary School, Cambridge, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Didelot</LastName>
                    <ForeName>Xavier</ForeName>
                    <Initials>X</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analyses and Modelling, School of Public Health, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Frost</LastName>
                    <ForeName>Simon</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Veterinary Medicine, Cambridge Veterinary School, Cambridge, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Hossain</LastName>
                    <ForeName>A S Md Mukarram</ForeName>
                    <Initials>AS</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Veterinary Medicine, Cambridge Veterinary School, Cambridge, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Joy</LastName>
                    <ForeName>Jeffrey B</ForeName>
                    <Initials>JB</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Medicine, University of British Columbia, Vancouver, BC, Canada.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kendall</LastName>
                    <ForeName>Michelle</ForeName>
                    <Initials>M</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Mathematics, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kühnert</LastName>
                    <ForeName>Denise</ForeName>
                    <Initials>D</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Leventhal</LastName>
                    <ForeName>Gabriel E</ForeName>
                    <Initials>GE</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Civil and Environmental Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Liang</LastName>
                    <ForeName>Richard</ForeName>
                    <Initials>R</Initials>
                    <AffiliationInfo>
                        <Affiliation>British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Plazzotta</LastName>
                    <ForeName>Giacomo</ForeName>
                    <Initials>G</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Mathematics, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Poon</LastName>
                    <ForeName>Art F Y</ForeName>
                    <Initials>AF</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Pathology &amp; Laboratory Medicine, Western University, Ontario, Canada.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Rasmussen</LastName>
                    <ForeName>David A</ForeName>
                    <Initials>DA</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Stadler</LastName>
                    <ForeName>Tanja</ForeName>
                    <Initials>T</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Volz</LastName>
                    <ForeName>Erik</ForeName>
                    <Initials>E</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analyses and Modelling, School of Public Health, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Weis</LastName>
                    <ForeName>Caroline</ForeName>
                    <Initials>C</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Leigh Brown</LastName>
                    <ForeName>Andrew J</ForeName>
                    <Initials>AJ</Initials>
                    <AffiliationInfo>
                        <Affiliation>School of Biological Sciences, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Fraser</LastName>
                    <ForeName>Christophe</ForeName>
                    <Initials>C</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analyses and Modelling, School of Public Health, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Nuffield Department of Medicine, Li Ka Shing Centre for Health Information and Discovery, Oxford Big Data Institute, University of Oxford, Oxford, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <CollectiveName>PANGEA-HIV Consortium</CollectiveName>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>MR/J013862/1</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>U01 GM110749</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>UM1 AI068619</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>10</Month>
                <Day>07</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>Mol Biol Evol</MedlineTA>
            <NlmUniqueID>8501455</NlmUniqueID>
            <ISSNLinking>0737-4038</ISSNLinking>
        </MedlineJournalInfo>
        <KeywordList Owner="NOTNLM">
            <Keyword MajorTopicYN="N">HIV transmission and prevention</Keyword>
            <Keyword MajorTopicYN="N">molecular epidemiology of infectious diseases</Keyword>
            <Keyword MajorTopicYN="N">viral phylogenetic methods validation</Keyword>
        </KeywordList>
    </MedlineCitation>
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        <History>
            <PubMedPubDate PubStatus="entrez">
                <Year>2017</Year>
                <Month>1</Month>
                <Day>6</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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            <PubMedPubDate PubStatus="pubmed">
                <Year>2017</Year>
                <Month>1</Month>
                <Day>6</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="medline">
                <Year>2017</Year>
                <Month>1</Month>
                <Day>6</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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        </History>
        <PublicationStatus>ppublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28053012</ArticleId>
            <ArticleId IdType="pii">msw217</ArticleId>
            <ArticleId IdType="doi">10.1093/molbev/msw217</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM">
        <PMID Version="1">27990325</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>12</Month>
            <Day>19</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>03</Month>
            <Day>23</Day>
        </DateRevised>
        <Article PubModel="Print-Electronic">
            <Journal>
                <JournalIssue CitedMedium="Print">
                    <Volume>4</Volume>
                    <PubDate>
                        <Year>2015</Year>
                    </PubDate>
                </JournalIssue>
                <Title>EPJ data science</Title>
                <ISOAbbreviation>EPJ Data Sci</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Enhancing disease surveillance with novel data streams: challenges and opportunities.</ArticleTitle>
            <ELocationID EIdType="pii" ValidYN="Y">17</ELocationID>
            <Abstract>
                <AbstractText>Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Althouse</LastName>
                    <ForeName>Benjamin M</ForeName>
                    <Initials>BM</Initials>
                    <AffiliationInfo>
                        <Affiliation>Santa Fe Institute, Santa Fe, NM, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Scarpino</LastName>
                    <ForeName>Samuel V</ForeName>
                    <Initials>SV</Initials>
                    <AffiliationInfo>
                        <Affiliation>Santa Fe Institute, Santa Fe, NM, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Meyers</LastName>
                    <ForeName>Lauren Ancel</ForeName>
                    <Initials>LA</Initials>
                    <AffiliationInfo>
                        <Affiliation>Santa Fe Institute, Santa Fe, NM, USA.; The University of Texas at Austin, Austin, TX, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ayers</LastName>
                    <ForeName>John W</ForeName>
                    <Initials>JW</Initials>
                    <AffiliationInfo>
                        <Affiliation>San Diego State University, San Diego, CA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Bargsten</LastName>
                    <ForeName>Marisa</ForeName>
                    <Initials>M</Initials>
                    <AffiliationInfo>
                        <Affiliation>New Mexico Department of Health, Santa Fe, NM, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Baumbach</LastName>
                    <ForeName>Joan</ForeName>
                    <Initials>J</Initials>
                    <AffiliationInfo>
                        <Affiliation>New Mexico Department of Health, Santa Fe, NM, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Brownstein</LastName>
                    <ForeName>John S</ForeName>
                    <Initials>JS</Initials>
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            <ELocationID EIdType="pii" ValidYN="Y">S0140-6736(16)32621-6</ELocationID>
            <ELocationID EIdType="doi" ValidYN="Y">10.1016/S0140-6736(16)32621-6</ELocationID>
            <Abstract>
                <AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">rVSV-ZEBOV is a recombinant, replication competent vesicular stomatitis virus-based candidate vaccine expressing a surface glycoprotein of Zaire Ebolavirus. We tested the effect of rVSV-ZEBOV in preventing Ebola virus disease in contacts and contacts of contacts of recently confirmed cases in Guinea, west Africa.</AbstractText>
                <AbstractText Label="METHODS" NlmCategory="METHODS">We did an open-label, cluster-randomised ring vaccination trial (Ebola ça Suffit!) in the communities of Conakry and eight surrounding prefectures in the Basse-Guinée region of Guinea, and in Tomkolili and Bombali in Sierra Leone. We assessed the efficacy of a single intramuscular dose of rVSV-ZEBOV (2×10(7) plaque-forming units administered in the deltoid muscle) in the prevention of laboratory confirmed Ebola virus disease. After confirmation of a case of Ebola virus disease, we definitively enumerated on a list a ring (cluster) of all their contacts and contacts of contacts including named contacts and contacts of contacts who were absent at the time of the trial team visit. The list was archived, then we randomly assigned clusters (1:1) to either immediate vaccination or delayed vaccination (21 days later) of all eligible individuals (eg, those aged ≥18 years and not pregnant, breastfeeding, or severely ill). An independent statistician generated the assignment sequence using block randomisation with randomly varying blocks, stratified by location (urban vs rural) and size of rings (≤20 individuals vs &gt;20 individuals). Ebola response teams and laboratory workers were unaware of assignments. After a recommendation by an independent data and safety monitoring board, randomisation was stopped and immediate vaccination was also offered to children aged 6-17 years and all identified rings. The prespecified primary outcome was a laboratory confirmed case of Ebola virus disease with onset 10 days or more from randomisation. The primary analysis compared the incidence of Ebola virus disease in eligible and vaccinated individuals assigned to immediate vaccination versus eligible contacts and contacts of contacts assigned to delayed vaccination. This trial is registered with the Pan African Clinical Trials Registry, number PACTR201503001057193.</AbstractText>
                <AbstractText Label="FINDINGS" NlmCategory="RESULTS">In the randomised part of the trial we identified 4539 contacts and contacts of contacts in 51 clusters randomly assigned to immediate vaccination (of whom 3232 were eligible, 2151 consented, and 2119 were immediately vaccinated) and 4557 contacts and contacts of contacts in 47 clusters randomly assigned to delayed vaccination (of whom 3096 were eligible, 2539 consented, and 2041 were vaccinated 21 days after randomisation). No cases of Ebola virus disease occurred 10 days or more after randomisation among randomly assigned contacts and contacts of contacts vaccinated in immediate clusters versus 16 cases (7 clusters affected) among all eligible individuals in delayed clusters. Vaccine efficacy was 100% (95% CI 68·9-100·0, p=0·0045), and the calculated intraclass correlation coefficient was 0·035. Additionally, we defined 19 non-randomised clusters in which we enumerated 2745 contacts and contacts of contacts, 2006 of whom were eligible and 1677 were immediately vaccinated, including 194 children. The evidence from all 117 clusters showed that no cases of Ebola virus disease occurred 10 days or more after randomisation among all immediately vaccinated contacts and contacts of contacts versus 23 cases (11 clusters affected) among all eligible contacts and contacts of contacts in delayed plus all eligible contacts and contacts of contacts never vaccinated in immediate clusters. The estimated vaccine efficacy here was 100% (95% CI 79·3-100·0, p=0·0033). 52% of contacts and contacts of contacts assigned to immediate vaccination and in non-randomised clusters received the vaccine immediately; vaccination protected both vaccinated and unvaccinated people in those clusters. 5837 individuals in total received the vaccine (5643 adults and 194 children), and all vaccinees were followed up for 84 days. 3149 (53·9%) of 5837 individuals reported at least one adverse event in the 14 days after vaccination; these were typically mild (87·5% of all 7211 adverse events). Headache (1832 [25·4%]), fatigue (1361 [18·9%]), and muscle pain (942 [13·1%]) were the most commonly reported adverse events in this period across all age groups. 80 serious adverse events were identified, of which two were judged to be related to vaccination (one febrile reaction and one anaphylaxis) and one possibly related (influenza-like illness); all three recovered without sequelae.</AbstractText>
                <AbstractText Label="INTERPRETATION" NlmCategory="CONCLUSIONS">The results add weight to the interim assessment that rVSV-ZEBOV offers substantial protection against Ebola virus disease, with no cases among vaccinated individuals from day 10 after vaccination in both randomised and non-randomised clusters.</AbstractText>
                <AbstractText Label="FUNDING" NlmCategory="BACKGROUND">WHO, UK Wellcome Trust, the UK Government through the Department of International Development, Médecins Sans Frontières, Norwegian Ministry of Foreign Affairs (through the Research Council of Norway's GLOBVAC programme), and the Canadian Government (through the Public Health Agency of Canada, Canadian Institutes of Health Research, International Development Research Centre and Department of Foreign Affairs, Trade and Development).</AbstractText>
                <CopyrightInformation>Copyright © 2017 World Health Organization. Published by Elsevier Ltd/Inc/BV. All rights reserved. Published by Elsevier Ltd.. All rights reserved.</CopyrightInformation>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Henao-Restrepo</LastName>
                    <ForeName>Ana Maria</ForeName>
                    <Initials>AM</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO, Geneva, Switzerland. Electronic address: henaorestrepoa@who.int.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Camacho</LastName>
                    <ForeName>Anton</ForeName>
                    <Initials>A</Initials>
                    <AffiliationInfo>
                        <Affiliation>Faculty of Epidemiology and Population Health, London School of Hygiene &amp; Tropical Medicine, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Longini</LastName>
                    <ForeName>Ira M</ForeName>
                    <Initials>IM</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Biostatistics, University of Florida, Gainesville, FL, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Watson</LastName>
                    <ForeName>Conall H</ForeName>
                    <Initials>CH</Initials>
                    <AffiliationInfo>
                        <Affiliation>Faculty of Epidemiology and Population Health, London School of Hygiene &amp; Tropical Medicine, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Edmunds</LastName>
                    <ForeName>W John</ForeName>
                    <Initials>WJ</Initials>
                    <AffiliationInfo>
                        <Affiliation>Faculty of Epidemiology and Population Health, London School of Hygiene &amp; Tropical Medicine, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Egger</LastName>
                    <ForeName>Matthias</ForeName>
                    <Initials>M</Initials>
                    <AffiliationInfo>
                        <Affiliation>Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Carroll</LastName>
                    <ForeName>Miles W</ForeName>
                    <Initials>MW</Initials>
                    <AffiliationInfo>
                        <Affiliation>Public Health England, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Dean</LastName>
                    <ForeName>Natalie E</ForeName>
                    <Initials>NE</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Biostatistics, University of Florida, Gainesville, FL, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Diatta</LastName>
                    <ForeName>Ibrahima</ForeName>
                    <Initials>I</Initials>
                    <AffiliationInfo>
                        <Affiliation>Clinical Trials Unit Bern, University of Bern, Bern, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Doumbia</LastName>
                    <ForeName>Moussa</ForeName>
                    <Initials>M</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO, Geneva, Switzerland; Centre National d'Appui à la Lutte contre la Maladie, Bamako, Mali.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Draguez</LastName>
                    <ForeName>Bertrand</ForeName>
                    <Initials>B</Initials>
                    <AffiliationInfo>
                        <Affiliation>Médecins Sans Frontières, Brussels, Belgium.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Duraffour</LastName>
                    <ForeName>Sophie</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Bernard Nocht Institute for Tropical Medicine, University of Hamburg, Hamburg, Germany.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Enwere</LastName>
                    <ForeName>Godwin</ForeName>
                    <Initials>G</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO, Geneva, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Grais</LastName>
                    <ForeName>Rebecca</ForeName>
                    <Initials>R</Initials>
                    <AffiliationInfo>
                        <Affiliation>Epicentre, Paris, France.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Gunther</LastName>
                    <ForeName>Stephan</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Bernard Nocht Institute for Tropical Medicine, University of Hamburg, Hamburg, Germany.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Gsell</LastName>
                    <ForeName>Pierre-Stéphane</ForeName>
                    <Initials>PS</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO, Geneva, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Hossmann</LastName>
                    <ForeName>Stefanie</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Clinical Trials Unit Bern, University of Bern, Bern, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Watle</LastName>
                    <ForeName>Sara Viksmoen</ForeName>
                    <Initials>SV</Initials>
                    <AffiliationInfo>
                        <Affiliation>Division of Infectious Disease Control, Norwegian Institute of Public Health, Oslo, Norway.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kondé</LastName>
                    <ForeName>Mandy Kader</ForeName>
                    <Initials>MK</Initials>
                    <AffiliationInfo>
                        <Affiliation>Center Of Excellence For Training, Research On Malaria &amp; Priority Diseases In Guinea, Conakry, Guinea.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kéïta</LastName>
                    <ForeName>Sakoba</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Ebola Response, Ministry of Health, Conakry, Guinea.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kone</LastName>
                    <ForeName>Souleymane</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO, Geneva, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kuisma</LastName>
                    <ForeName>Eewa</ForeName>
                    <Initials>E</Initials>
                    <AffiliationInfo>
                        <Affiliation>Bernard Nocht Institute for Tropical Medicine, University of Hamburg, Hamburg, Germany.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Levine</LastName>
                    <ForeName>Myron M</ForeName>
                    <Initials>MM</Initials>
                    <AffiliationInfo>
                        <Affiliation>Center for Vaccine Development, University of Maryland School of Medicine, Baltimore, MD, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Mandal</LastName>
                    <ForeName>Sema</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Public Health England, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Mauget</LastName>
                    <ForeName>Thomas</ForeName>
                    <Initials>T</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO, Geneva, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Norheim</LastName>
                    <ForeName>Gunnstein</ForeName>
                    <Initials>G</Initials>
                    <AffiliationInfo>
                        <Affiliation>Division of Infectious Disease Control, Norwegian Institute of Public Health, Oslo, Norway.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Riveros</LastName>
                    <ForeName>Ximena</ForeName>
                    <Initials>X</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO, Geneva, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Soumah</LastName>
                    <ForeName>Aboubacar</ForeName>
                    <Initials>A</Initials>
                    <AffiliationInfo>
                        <Affiliation>Epicentre, Paris, France.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Trelle</LastName>
                    <ForeName>Sven</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Clinical Trials Unit Bern, University of Bern, Bern, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Vicari</LastName>
                    <ForeName>Andrea S</ForeName>
                    <Initials>AS</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO, Geneva, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Røttingen</LastName>
                    <ForeName>John-Arne</ForeName>
                    <Initials>JA</Initials>
                    <AffiliationInfo>
                        <Affiliation>Division of Infectious Disease Control, Norwegian Institute of Public Health, Oslo, Norway; Department of Health and Society, University of Oslo, Norway; Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA; Coalition for Epidemic Preparedness Innovations, care of Norwegian Institute of Public Health, Oslo, Norway.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kieny</LastName>
                    <ForeName>Marie-Paule</ForeName>
                    <Initials>MP</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO, Geneva, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>R37 AI032042</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U54 GM111274</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>23</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>England</Country>
            <MedlineTA>Lancet</MedlineTA>
            <NlmUniqueID>2985213R</NlmUniqueID>
            <ISSNLinking>0140-6736</ISSNLinking>
        </MedlineJournalInfo>
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    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="received">
                <Year>2016</Year>
                <Month>10</Month>
                <Day>10</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="revised">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>25</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>06</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>27</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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                <Year>2016</Year>
                <Month>12</Month>
                <Day>27</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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                <Year>2016</Year>
                <Month>12</Month>
                <Day>27</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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        </History>
        <PublicationStatus>ppublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28017403</ArticleId>
            <ArticleId IdType="pii">S0140-6736(16)32621-6</ArticleId>
            <ArticleId IdType="doi">10.1016/S0140-6736(16)32621-6</ArticleId>
            <ArticleId IdType="pmc">PMC5364328</ArticleId>
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<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">27716818</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>10</Month>
            <Day>07</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>03</Month>
            <Day>07</Day>
        </DateRevised>
        <Article PubModel="Electronic-eCollection">
            <Journal>
                <ISSN IssnType="Electronic">1932-6203</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>11</Volume>
                    <Issue>10</Issue>
                    <PubDate>
                        <Year>2016</Year>
                    </PubDate>
                </JournalIssue>
                <Title>PloS one</Title>
                <ISOAbbreviation>PLoS ONE</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Impact of Age and Sex on CD4+ Cell Count Trajectories following Treatment Initiation: An Analysis of the Tanzanian HIV Treatment Database.</ArticleTitle>
            <Pagination>
                <MedlinePgn>e0164148</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0164148</ELocationID>
            <Abstract>
                <AbstractText Label="OBJECTIVE" NlmCategory="OBJECTIVE">New guidelines recommend that all HIV-infected individuals initiate antiretroviral treatment (ART) immediately following diagnosis. This study describes how immune reconstitution varies by gender and age to help identify poorly reconstituting subgroups and inform targeted testing initiatives.</AbstractText>
                <AbstractText Label="DESIGN" NlmCategory="METHODS">Longitudinal data from the outpatient monitoring system of the National AIDS Control Program in Tanzania.</AbstractText>
                <AbstractText Label="METHODS" NlmCategory="METHODS">An asymptotic nonlinear mixed effects model was fit to post-treatment CD4+ cell count trajectories, allowing for fixed effects of age and sex, and an age by sex interaction.</AbstractText>
                <AbstractText Label="RESULTS" NlmCategory="RESULTS">Across 220,544 clinic visits from 32,069 HIV-infected patients, age- and sex-specific average CD4+ cell count at ART initiation ranged from 83-136 cells/mm3, long term asymptotic CD4+ cell count ranged from 301-389 cells/mm3, and time to half of maximal CD4+ reconstitution ranged from 3.57-5.68 months. CD4+ cell count at ART initiation and asymptotic CD4+ cell count were 1.28 (95% CI: 1.18-1.40) and 1.25 (95% CI: 1.20-1.31) times higher, respectively, for females compared to males in the youngest age group (19-29 years). Older patients started treatment at higher CD4+ counts but experienced slower CD4+ recovery than younger adults. Treatment initiation at greater CD4+ cell counts was correlated with greater asymptotic CD4+ cell counts within all sex and age groups.</AbstractText>
                <AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">Older adults should initiate care early in disease progression because total immune reconstitution potential and rate of reconstitution appears to decrease with age. Targeted HIV testing and care linkage remains crucial for patient populations who tend to initiate treatment at lower CD4+ cell counts, including males and younger adults.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Means</LastName>
                    <ForeName>Arianna R</ForeName>
                    <Initials>AR</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Global Health, University of Washington, Seattle, Washington, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Risher</LastName>
                    <ForeName>Kathryn A</ForeName>
                    <Initials>KA</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ujeneza</LastName>
                    <ForeName>Eva L</ForeName>
                    <Initials>EL</Initials>
                    <AffiliationInfo>
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                <Month>04</Month>
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            <ArticleId IdType="pubmed">27716818</ArticleId>
            <ArticleId IdType="doi">10.1371/journal.pone.0164148</ArticleId>
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<PubmedArticle>
    <MedlineCitation Status="MEDLINE" Owner="NLM">
        <PMID Version="1">27068686</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>04</Month>
            <Day>12</Day>
        </DateCreated>
        <DateCompleted>
            <Year>2016</Year>
            <Month>12</Month>
            <Day>13</Day>
        </DateCompleted>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Electronic">
            <Journal>
                <ISSN IssnType="Electronic">1475-2875</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>15</Volume>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>Apr</Month>
                        <Day>12</Day>
                    </PubDate>
                </JournalIssue>
                <Title>Malaria journal</Title>
                <ISOAbbreviation>Malar. J.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Key traveller groups of relevance to spatial malaria transmission: a survey of movement patterns in four sub-Saharan African countries.</ArticleTitle>
            <Pagination>
                <MedlinePgn>200</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1186/s12936-016-1252-3</ELocationID>
            <Abstract>
                <AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">As malaria prevalence declines in many parts of the world due to widescale control efforts and as drug-resistant parasites begin to emerge, a quantitative understanding of human movement is becoming increasingly relevant to malaria control. However, despite its importance, significant knowledge gaps remain regarding human movement, particularly in sub-Saharan Africa.</AbstractText>
                <AbstractText Label="METHODS" NlmCategory="METHODS">A quantitative survey of human movement patterns was conducted in four countries in sub-Saharan Africa: Mali, Burkina Faso, Zambia, and Tanzania, with three to five survey locations chosen in each country. Questions were included on demographic and trip details, malaria risk behaviour, children accompanying travellers, and mobile phone usage to enable phone signal data to be better correlated with movement. A total of 4352 individuals were interviewed and 6411 trips recorded.</AbstractText>
                <AbstractText Label="RESULTS" NlmCategory="RESULTS">A cluster analysis of trips highlighted two distinct traveller groups of relevance to malaria transmission: women travelling with children (in all four countries) and youth workers (in Mali). Women travelling with children were more likely to travel to areas of relatively high malaria prevalence in Mali (OR = 4.46, 95% CI = 3.42-5.83), Burkina Faso (OR = 1.58, 95% CI = 1.23-1.58), Zambia (OR = 1.50, 95% CI = 1.20-1.89), and Tanzania (OR = 2.28, 95% CI = 1.71-3.05) compared to other travellers. They were also more likely to own bed nets in Burkina Faso (OR = 1.77, 95% CI = 1.25-2.53) and Zambia (OR = 1.74, 95% CI = 1.34 2.27), and less likely to own a mobile phone in Mali (OR = 0.50, 95% CI = 0.39-0.65), Burkina Faso (OR = 0.39, 95% CI = 0.30-0.52), and Zambia (OR = 0.60, 95% CI = 0.47-0.76). Malian youth workers were more likely to travel to areas of relatively high malaria prevalence (OR = 23, 95% CI = 17-31) and for longer durations (mean of 70 days cf 21 days, p &lt; 0.001) compared to other travellers.</AbstractText>
                <AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Women travelling with children were a remarkably consistent traveller group across all four countries surveyed. They are expected to contribute greatly towards spatial malaria transmission because the children they travel with tend to have high parasite prevalence. Youth workers were a significant traveller group in Mali and are expected to contribute greatly to spatial malaria transmission because their movements correlate with seasonal rains and hence peak mosquito densities. Interventions aimed at interrupting spatial transmission of parasites should consider these traveller groups.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Marshall</LastName>
                    <ForeName>John M</ForeName>
                    <Initials>JM</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, MRC Center for Outbreak Analysis and Modelling, Imperial College London, London, UK. john.marshall@berkeley.edu.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Divisions of Biostatistics and Epidemiology, School of Public Health, University of California, Berkeley, CA, USA. john.marshall@berkeley.edu.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Touré</LastName>
                    <ForeName>Mahamoudou</ForeName>
                    <Initials>M</Initials>
                    <AffiliationInfo>
                        <Affiliation>Malaria Research and Training Center, University of Bamako, Bamako, Mali.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ouédraogo</LastName>
                    <ForeName>André Lin</ForeName>
                    <Initials>AL</Initials>
                    <AffiliationInfo>
                        <Affiliation>Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Institute for Disease Modeling, Bellevue, WA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ndhlovu</LastName>
                    <ForeName>Micky</ForeName>
                    <Initials>M</Initials>
                    <AffiliationInfo>
                        <Affiliation>Chainama College of Health Sciences, Lusaka, Zambia.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kiware</LastName>
                    <ForeName>Samson S</ForeName>
                    <Initials>SS</Initials>
                    <AffiliationInfo>
                        <Affiliation>Environmental Health and Ecological Sciences Thematic Group, Ifakara Health Institute, Dar es Salaam, Tanzania.</Affiliation>
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                </Author>
                <Author ValidYN="Y">
                    <LastName>Rezai</LastName>
                    <ForeName>Ashley</ForeName>
                    <Initials>A</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, MRC Center for Outbreak Analysis and Modelling, Imperial College London, London, UK.</Affiliation>
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                </Author>
                <Author ValidYN="Y">
                    <LastName>Nkhama</LastName>
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                    <Initials>E</Initials>
                    <AffiliationInfo>
                        <Affiliation>Chainama College of Health Sciences, Lusaka, Zambia.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Griffin</LastName>
                    <ForeName>Jamie T</ForeName>
                    <Initials>JT</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, MRC Center for Outbreak Analysis and Modelling, Imperial College London, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Hollingsworth</LastName>
                    <ForeName>T Deirdre</ForeName>
                    <Initials>TD</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, MRC Center for Outbreak Analysis and Modelling, Imperial College London, London, UK.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>School of Life Sciences, University of Warwick, Warwick, Coventry, UK.</Affiliation>
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                <Author ValidYN="Y">
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                        <Affiliation>Malaria Research and Training Center, University of Bamako, Bamako, Mali.</Affiliation>
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                        <Affiliation>Department of Infectious Disease Epidemiology, MRC Center for Outbreak Analysis and Modelling, Imperial College London, London, UK.</Affiliation>
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                        <Affiliation>Department of Infectious Disease Epidemiology, MRC Center for Outbreak Analysis and Modelling, Imperial College London, London, UK.</Affiliation>
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                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
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                <Grant>
                    <GrantID>MR/J012254/1</GrantID>
                    <Agency>Medical Research Council</Agency>
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                <Grant>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
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                    <GrantID>G1002284</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
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                <Grant>
                    <GrantID>MR/K010174/1</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
                <PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
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            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>04</Month>
                <Day>12</Day>
            </ArticleDate>
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            <Country>England</Country>
            <MedlineTA>Malar J</MedlineTA>
            <NlmUniqueID>101139802</NlmUniqueID>
            <ISSNLinking>1475-2875</ISSNLinking>
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            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D017954" MajorTopicYN="N">Africa South of the Sahara</DescriptorName>
                <QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D002648" MajorTopicYN="N">Child</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D002675" MajorTopicYN="N">Child, Preschool</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D007223" MajorTopicYN="N">Infant</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D007231" MajorTopicYN="N">Infant, Newborn</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D007407" MajorTopicYN="N">Interviews as Topic</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D008288" MajorTopicYN="N">Malaria</DescriptorName>
                <QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName>
                <QualifierName UI="Q000635" MajorTopicYN="Y">transmission</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D009068" MajorTopicYN="N">Movement</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D014195" MajorTopicYN="Y">Travel</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName>
            </MeshHeading>
        </MeshHeadingList>
        <OtherID Source="NLM">PMC4828820</OtherID>
        <KeywordList Owner="NOTNLM">
            <Keyword MajorTopicYN="N">Burkina Faso</Keyword>
            <Keyword MajorTopicYN="N">Cluster analysis</Keyword>
            <Keyword MajorTopicYN="N">Mali</Keyword>
            <Keyword MajorTopicYN="N">Mobile phones</Keyword>
            <Keyword MajorTopicYN="N">Plasmodium falciparum</Keyword>
            <Keyword MajorTopicYN="N">Spatial transmission</Keyword>
            <Keyword MajorTopicYN="N">Tanzania</Keyword>
            <Keyword MajorTopicYN="N">Women with children</Keyword>
            <Keyword MajorTopicYN="N">Youth workers</Keyword>
            <Keyword MajorTopicYN="N">Zambia</Keyword>
        </KeywordList>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="received">
                <Year>2016</Year>
                <Month>01</Month>
                <Day>07</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2016</Year>
                <Month>03</Month>
                <Day>30</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="entrez">
                <Year>2016</Year>
                <Month>4</Month>
                <Day>13</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2016</Year>
                <Month>4</Month>
                <Day>14</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="medline">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>15</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
        </History>
        <PublicationStatus>epublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">27068686</ArticleId>
            <ArticleId IdType="doi">10.1186/s12936-016-1252-3</ArticleId>
            <ArticleId IdType="pii">10.1186/s12936-016-1252-3</ArticleId>
            <ArticleId IdType="pmc">PMC4828820</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="MEDLINE" Owner="NLM">
        <PMID Version="1">27417495</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>08</Month>
            <Day>12</Day>
        </DateCreated>
        <DateCompleted>
            <Year>2016</Year>
            <Month>09</Month>
            <Day>02</Day>
        </DateCompleted>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>08</Day>
        </DateRevised>
        <Article PubModel="Print-Electronic">
            <Journal>
                <ISSN IssnType="Electronic">1095-9203</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>353</Volume>
                    <Issue>6300</Issue>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>Aug</Month>
                        <Day>12</Day>
                    </PubDate>
                </JournalIssue>
                <Title>Science (New York, N.Y.)</Title>
                <ISOAbbreviation>Science</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Assessing the global threat from Zika virus.</ArticleTitle>
            <Pagination>
                <MedlinePgn>aaf8160</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1126/science.aaf8160</ELocationID>
            <ELocationID EIdType="pii" ValidYN="Y">aaf8160</ELocationID>
            <Abstract>
                <AbstractText>First discovered in 1947, Zika virus (ZIKV) infection remained a little-known tropical disease until 2015, when its apparent association with a considerable increase in the incidence of microcephaly in Brazil raised alarms worldwide. There is limited information on the key factors that determine the extent of the global threat from ZIKV infection and resulting complications. Here, we review what is known about the epidemiology, natural history, and public health effects of ZIKV infection, the empirical basis for this knowledge, and the critical knowledge gaps that need to be filled.</AbstractText>
                <CopyrightInformation>Copyright © 2016, American Association for the Advancement of Science.</CopyrightInformation>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Lessler</LastName>
                    <ForeName>Justin</ForeName>
                    <Initials>J</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. justin@jhu.edu.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Chaisson</LastName>
                    <ForeName>Lelia H</ForeName>
                    <Initials>LH</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kucirka</LastName>
                    <ForeName>Lauren M</ForeName>
                    <Initials>LM</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Bi</LastName>
                    <ForeName>Qifang</ForeName>
                    <Initials>Q</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Grantz</LastName>
                    <ForeName>Kyra</ForeName>
                    <Initials>K</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Biology, Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Salje</LastName>
                    <ForeName>Henrik</ForeName>
                    <Initials>H</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Carcelen</LastName>
                    <ForeName>Andrea C</ForeName>
                    <Initials>AC</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ott</LastName>
                    <ForeName>Cassandra T</ForeName>
                    <Initials>CT</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Sheffield</LastName>
                    <ForeName>Jeanne S</ForeName>
                    <Initials>JS</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ferguson</LastName>
                    <ForeName>Neil M</ForeName>
                    <Initials>NM</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Medicine, School of Public Health, Imperial College London, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cummings</LastName>
                    <ForeName>Derek A T</ForeName>
                    <Initials>DA</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Biology, Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Metcalf</LastName>
                    <ForeName>C Jessica E</ForeName>
                    <Initials>CJ</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA. Office of Population Research, Princeton University, Princeton, NJ, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Rodriguez-Barraquer</LastName>
                    <ForeName>Isabel</ForeName>
                    <Initials>I</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>MR/K010174/1</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>P2C HD047879</GrantID>
                    <Acronym>HD</Acronym>
                    <Agency>NICHD NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
                <PublicationType UI="D016454">Review</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>07</Month>
                <Day>14</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>Science</MedlineTA>
            <NlmUniqueID>0404511</NlmUniqueID>
            <ISSNLinking>0036-8075</ISSNLinking>
        </MedlineJournalInfo>
        <CitationSubset>IM</CitationSubset>
        <MeshHeadingList>
            <MeshHeading>
                <DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D035843" MajorTopicYN="N">Biomedical Research</DescriptorName>
                <QualifierName UI="Q000639" MajorTopicYN="N">trends</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D001938" MajorTopicYN="N" Type="Geographic">Brazil</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D003465" MajorTopicYN="N">Culex</DescriptorName>
                <QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D015195" MajorTopicYN="N">Drug Design</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D014943" MajorTopicYN="N">Global Health</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D015994" MajorTopicYN="N">Incidence</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D008831" MajorTopicYN="N">Microcephaly</DescriptorName>
                <QualifierName UI="Q000821" MajorTopicYN="Y">virology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D010802" MajorTopicYN="N">Phylogeny</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D011247" MajorTopicYN="N">Pregnancy</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D011251" MajorTopicYN="N">Pregnancy Complications, Infectious</DescriptorName>
                <QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D011634" MajorTopicYN="N">Public Health</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D000071244" MajorTopicYN="Y">Zika Virus</DescriptorName>
                <QualifierName UI="Q000145" MajorTopicYN="N">classification</QualifierName>
                <QualifierName UI="Q000472" MajorTopicYN="N">pathogenicity</QualifierName>
                <QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D000071243" MajorTopicYN="N">Zika Virus Infection</DescriptorName>
                <QualifierName UI="Q000150" MajorTopicYN="N">complications</QualifierName>
                <QualifierName UI="Q000517" MajorTopicYN="Y">prevention &amp; control</QualifierName>
                <QualifierName UI="Q000635" MajorTopicYN="Y">transmission</QualifierName>
            </MeshHeading>
        </MeshHeadingList>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="entrez">
                <Year>2016</Year>
                <Month>7</Month>
                <Day>16</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2016</Year>
                <Month>7</Month>
                <Day>16</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="medline">
                <Year>2016</Year>
                <Month>9</Month>
                <Day>3</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
        </History>
        <PublicationStatus>ppublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">27417495</ArticleId>
            <ArticleId IdType="pii">science.aaf8160</ArticleId>
            <ArticleId IdType="doi">10.1126/science.aaf8160</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="In-Data-Review" Owner="NLM">
        <PMID Version="1">27457935</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>08</Month>
            <Day>11</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Print-Electronic">
            <Journal>
                <ISSN IssnType="Electronic">1091-6490</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>113</Volume>
                    <Issue>32</Issue>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>Aug</Month>
                        <Day>09</Day>
                    </PubDate>
                </JournalIssue>
                <Title>Proceedings of the National Academy of Sciences of the United States of America</Title>
                <ISOAbbreviation>Proc. Natl. Acad. Sci. U.S.A.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Unraveling the drivers of MERS-CoV transmission.</ArticleTitle>
            <Pagination>
                <MedlinePgn>9081-6</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1073/pnas.1519235113</ELocationID>
            <Abstract>
                <AbstractText>With more than 1,700 laboratory-confirmed infections, Middle East respiratory syndrome coronavirus (MERS-CoV) remains a significant threat for public health. However, the lack of detailed data on modes of transmission from the animal reservoir and between humans means that the drivers of MERS-CoV epidemics remain poorly characterized. Here, we develop a statistical framework to provide a comprehensive analysis of the transmission patterns underlying the 681 MERS-CoV cases detected in the Kingdom of Saudi Arabia (KSA) between January 2013 and July 2014. We assess how infections from the animal reservoir, the different levels of mixing, and heterogeneities in transmission have contributed to the buildup of MERS-CoV epidemics in KSA. We estimate that 12% [95% credible interval (CI): 9%, 15%] of cases were infected from the reservoir, the rest via human-to-human transmission in clusters (60%; CI: 57%, 63%), within (23%; CI: 20%, 27%), or between (5%; CI: 2%, 8%) regions. The reproduction number at the start of a cluster was 0.45 (CI: 0.33, 0.58) on average, but with large SD (0.53; CI: 0.35, 0.78). It was &gt;1 in 12% (CI: 6%, 18%) of clusters but fell by approximately one-half (47% CI: 34%, 63%) its original value after 10 cases on average. The ongoing exposure of humans to MERS-CoV from the reservoir is of major concern, given the continued risk of substantial outbreaks in health care systems. The approach we present allows the study of infectious disease transmission when data linking cases to each other remain limited and uncertain.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Cauchemez</LastName>
                    <ForeName>Simon</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, 75015 Paris, France; Centre National de la Recherche Scientifique, Unité de Recherche Associée 3012, 75015 Paris, France; Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015 Paris, France;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Nouvellet</LastName>
                    <ForeName>Pierre</ForeName>
                    <Initials>P</Initials>
                    <AffiliationInfo>
                        <Affiliation>Medical Research Council Centre for Outbreak Analysis and Modelling, Imperial College London, Faculty of Medicine, London W2 1PG, United Kingdom;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cori</LastName>
                    <ForeName>Anne</ForeName>
                    <Initials>A</Initials>
                    <AffiliationInfo>
                        <Affiliation>Medical Research Council Centre for Outbreak Analysis and Modelling, Imperial College London, Faculty of Medicine, London W2 1PG, United Kingdom;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Jombart</LastName>
                    <ForeName>Thibaut</ForeName>
                    <Initials>T</Initials>
                    <AffiliationInfo>
                        <Affiliation>Medical Research Council Centre for Outbreak Analysis and Modelling, Imperial College London, Faculty of Medicine, London W2 1PG, United Kingdom;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Garske</LastName>
                    <ForeName>Tini</ForeName>
                    <Initials>T</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0002-8952-4710</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Medical Research Council Centre for Outbreak Analysis and Modelling, Imperial College London, Faculty of Medicine, London W2 1PG, United Kingdom;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Clapham</LastName>
                    <ForeName>Hannah</ForeName>
                    <Initials>H</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Moore</LastName>
                    <ForeName>Sean</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Mills</LastName>
                    <ForeName>Harriet Linden</ForeName>
                    <Initials>HL</Initials>
                    <AffiliationInfo>
                        <Affiliation>Medical Research Council Centre for Outbreak Analysis and Modelling, Imperial College London, Faculty of Medicine, London W2 1PG, United Kingdom;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Salje</LastName>
                    <ForeName>Henrik</ForeName>
                    <Initials>H</Initials>
                    <AffiliationInfo>
                        <Affiliation>Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, 75015 Paris, France; Centre National de la Recherche Scientifique, Unité de Recherche Associée 3012, 75015 Paris, France; Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015 Paris, France; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Collins</LastName>
                    <ForeName>Caitlin</ForeName>
                    <Initials>C</Initials>
                    <AffiliationInfo>
                        <Affiliation>Medical Research Council Centre for Outbreak Analysis and Modelling, Imperial College London, Faculty of Medicine, London W2 1PG, United Kingdom;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Rodriquez-Barraquer</LastName>
                    <ForeName>Isabel</ForeName>
                    <Initials>I</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Riley</LastName>
                    <ForeName>Steven</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Medical Research Council Centre for Outbreak Analysis and Modelling, Imperial College London, Faculty of Medicine, London W2 1PG, United Kingdom;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Truelove</LastName>
                    <ForeName>Shaun</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Algarni</LastName>
                    <ForeName>Homoud</ForeName>
                    <Initials>H</Initials>
                    <AffiliationInfo>
                        <Affiliation>Ministry of Health, Riyadh 12234, Kingdom of Saudi Arabia;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Alhakeem</LastName>
                    <ForeName>Rafat</ForeName>
                    <Initials>R</Initials>
                    <AffiliationInfo>
                        <Affiliation>Ministry of Health, Riyadh 12234, Kingdom of Saudi Arabia;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>AlHarbi</LastName>
                    <ForeName>Khalid</ForeName>
                    <Initials>K</Initials>
                    <AffiliationInfo>
                        <Affiliation>Ministry of Health, Riyadh 12234, Kingdom of Saudi Arabia;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Turkistani</LastName>
                    <ForeName>Abdulhafiz</ForeName>
                    <Initials>A</Initials>
                    <AffiliationInfo>
                        <Affiliation>Ministry of Health, Riyadh 12234, Kingdom of Saudi Arabia;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Aguas</LastName>
                    <ForeName>Ricardo J</ForeName>
                    <Initials>RJ</Initials>
                    <AffiliationInfo>
                        <Affiliation>Medical Research Council Centre for Outbreak Analysis and Modelling, Imperial College London, Faculty of Medicine, London W2 1PG, United Kingdom;</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cummings</LastName>
                    <ForeName>Derek A T</ForeName>
                    <Initials>DA</Initials>
                    <AffiliationInfo>
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            <Language>eng</Language>
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                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
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            <Year>2017</Year>
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                <ISSN IssnType="Electronic">1742-5662</ISSN>
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                    <Volume>13</Volume>
                    <Issue>119</Issue>
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                        <Year>2016</Year>
                        <Month>Jun</Month>
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                <Title>Journal of the Royal Society, Interface</Title>
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                <AbstractText>Pigs have long been hypothesized to play a central role in the emergence of novel human influenza A virus (IAV) strains, by serving as mixing vessels for mammalian and avian variants. However, the key issue of viral persistence in swine populations at different scales is ill understood. We address this gap using epidemiological models calibrated against seroprevalence data from Dutch finishing pigs to estimate the 'critical herd size' (CHS) for IAV persistence. We then examine the viral phylogenetic evidence for persistence by comparing human and swine IAV. Models suggest a CHS of approximately 3000 pigs above which influenza was likely to persist, i.e. orders of magnitude lower than persistence thresholds for IAV and other acute viruses in humans. At national and regional scales, we found much stronger empirical signatures of prolonged persistence of IAV in swine compared with human populations. These striking levels of persistence in small populations are driven by the high recruitment rate of susceptible piglets, and have significant implications for management of swine and for overall patterns of genetic diversity of IAV.</AbstractText>
                <CopyrightInformation>© 2016 The Authors.</CopyrightInformation>
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                    <Initials>VE</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0003-1015-2289</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, CT 06520, USA Fogarty International Center, National Institutes of Health, Bethesda, MD 20850, USA virginia.pitzer@yale.edu.</Affiliation>
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                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College, London SW7 2AZ, UK.</Affiliation>
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        <KeywordList Owner="NOTNLM">
            <Keyword MajorTopicYN="N">influenza</Keyword>
            <Keyword MajorTopicYN="N">mathematical modelling</Keyword>
            <Keyword MajorTopicYN="N">surveillance</Keyword>
            <Keyword MajorTopicYN="N">swine</Keyword>
            <Keyword MajorTopicYN="N">transmission dynamics</Keyword>
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                <Month>02</Month>
                <Day>16</Day>
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                <Year>2016</Year>
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            <ArticleId IdType="pubmed">27358277</ArticleId>
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<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">27846234</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>11</Month>
            <Day>15</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
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            <Journal>
                <ISSN IssnType="Electronic">1549-1676</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>13</Volume>
                    <Issue>11</Issue>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>Nov</Month>
                    </PubDate>
                </JournalIssue>
                <Title>PLoS medicine</Title>
                <ISOAbbreviation>PLoS Med.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study.</ArticleTitle>
            <Pagination>
                <MedlinePgn>e1002170</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pmed.1002170</ELocationID>
            <Abstract>
                <AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">The ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved.</AbstractText>
                <AbstractText Label="METHODS AND FINDINGS" NlmCategory="RESULTS">Over 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola (&quot;cases&quot;) were asked if they had exposure to other potential Ebola cases (&quot;potential source contacts&quot;) in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO's response during the epidemic, and have been updated for publication. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p &lt; 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = -0.37, p &lt; 0.001) between R and the district proportion of hospitalised cases admitted within ≤4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the case line-list. Linking cases to the contacts who potentially infected them provided information on the transmission network. This revealed a high degree of heterogeneity in inferred transmissions, with only 20% of cases accounting for at least 73% of new infections, a phenomenon often called super-spreading. Multivariable regression models allowed us to identify predictors of being named as a potential source contact. These were similar for funeral and non-funeral contacts: severe symptoms, death, non-hospitalisation, older age, and travelling prior to symptom onset. Non-funeral exposures were strongly peaked around the death of the contact. There was evidence that hospitalisation reduced but did not eliminate onward exposures. We found that Ebola treatment units were better than other health care facilities at preventing exposure from hospitalised and deceased individuals. The principal limitation of our analysis is limited data quality, with cases not being entered into the database, cases not reporting exposures, or data being entered incorrectly (especially dates, and possible misclassifications).</AbstractText>
                <AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Achieving elimination of Ebola is challenging, partly because of super-spreading. Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. Continued real-time data capture, reporting, and analysis are vital to track transmission patterns, inform resource deployment, and thus hasten and maintain elimination of the virus from the human population.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <CollectiveName>International Ebola Response Team</CollectiveName>
                </Author>
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                    <LastName>Agua-Agum</LastName>
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                        <Affiliation>World Health Organization, Geneva, Switzerland.</Affiliation>
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                        <Affiliation>Centers for Disease Control and Prevention, Conakry, Guinea.</Affiliation>
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                    <LastName>Schafer</LastName>
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                    <AffiliationInfo>
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                        <Affiliation>Department for Infectious Disease Epidemiology, Robert Koch Institute, Berlin, Germany.</Affiliation>
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                <Author ValidYN="Y">
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                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.</Affiliation>
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                <Author ValidYN="Y">
                    <LastName>Wesseh</LastName>
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                        <Affiliation>World Health Organization, Freetown, Sierra Leone.</Affiliation>
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            </AuthorList>
            <Language>eng</Language>
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                <Grant>
                    <GrantID>G0800596</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>MR/J008761/1</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>MR/K010174/1</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>15</Day>
            </ArticleDate>
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            <Country>United States</Country>
            <MedlineTA>PLoS Med</MedlineTA>
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    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="received">
                <Year>2015</Year>
                <Month>09</Month>
                <Day>24</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2016</Year>
                <Month>10</Month>
                <Day>07</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="entrez">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>16</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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                <Year>2016</Year>
                <Month>11</Month>
                <Day>16</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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                <Year>2016</Year>
                <Month>11</Month>
                <Day>16</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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        <ArticleIdList>
            <ArticleId IdType="pubmed">27846234</ArticleId>
            <ArticleId IdType="doi">10.1371/journal.pmed.1002170</ArticleId>
            <ArticleId IdType="pii">PMEDICINE-D-15-02850</ArticleId>
            <ArticleId IdType="pmc">PMC5112802</ArticleId>
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<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">27855153</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>11</Month>
            <Day>17</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>08</Day>
        </DateRevised>
        <Article PubModel="Electronic-eCollection">
            <Journal>
                <ISSN IssnType="Electronic">1553-7358</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>12</Volume>
                    <Issue>11</Issue>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>Nov</Month>
                    </PubDate>
                </JournalIssue>
                <Title>PLoS computational biology</Title>
                <ISOAbbreviation>PLoS Comput. Biol.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Drivers of Inter-individual Variation in Dengue Viral Load Dynamics.</ArticleTitle>
            <Pagination>
                <MedlinePgn>e1005194</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pcbi.1005194</ELocationID>
            <Abstract>
                <AbstractText>Dengue is a vector-borne viral disease of humans that endemically circulates in many tropical and subtropical regions worldwide. Infection with dengue can result in a range of disease outcomes. A considerable amount of research has sought to improve our understanding of this variation in disease outcomes and to identify predictors of severe disease. Contributing to this research, patterns of viral load in dengue infected patients have been quantified, with analyses indicating that peak viral load levels, rates of viral load decline, and time to peak viremia are useful predictors of severe disease. Here, we take a complementary approach to understanding patterns of clinical manifestation and inter-individual variation in viral load dynamics. Specifically, we statistically fit mathematical within-host models of dengue to individual-level viral load data to test virological and immunological hypotheses explaining inter-individual variation in dengue viral load. We choose between alternative models using model selection criteria to determine which hypotheses are best supported by the data. We first show that the cellular immune response plays an important role in regulating viral load in secondary dengue infections. We then provide statistical support for the process of antibody-dependent enhancement (but not original antigenic sin) in the development of severe disease in secondary dengue infections. Finally, we show statistical support for serotype-specific differences in viral infectivity rates, with infectivity rates of dengue serotypes 2 and 3 exceeding those of serotype 1. These results contribute to our understanding of dengue viral load patterns and their relationship to the development of severe dengue disease. They further have implications for understanding how dengue transmissibility may depend on the immune status of infected individuals and the identity of the infecting serotype.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Ben-Shachar</LastName>
                    <ForeName>Rotem</ForeName>
                    <Initials>R</Initials>
                    <AffiliationInfo>
                        <Affiliation>Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Schmidler</LastName>
                    <ForeName>Scott</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Statistical Science, Duke University, Durham, North Carolina, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Computer Science, Duke University, Durham, North Carolina, United States of America.</Affiliation>
                    </AffiliationInfo>
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                <Author ValidYN="Y">
                    <LastName>Koelle</LastName>
                    <ForeName>Katia</ForeName>
                    <Initials>K</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Biology, Duke University, Durham, North Carolina, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>U54 GM111274</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>17</Day>
            </ArticleDate>
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            <Country>United States</Country>
            <MedlineTA>PLoS Comput Biol</MedlineTA>
            <NlmUniqueID>101238922</NlmUniqueID>
            <ISSNLinking>1553-734X</ISSNLinking>
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        <CoiStatement>The authors have declared that no competing interests exist.</CoiStatement>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="received">
                <Year>2016</Year>
                <Month>03</Month>
                <Day>08</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2016</Year>
                <Month>10</Month>
                <Day>12</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="entrez">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>18</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>18</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
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                <Year>2016</Year>
                <Month>11</Month>
                <Day>18</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
        </History>
        <PublicationStatus>epublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">27855153</ArticleId>
            <ArticleId IdType="doi">10.1371/journal.pcbi.1005194</ArticleId>
            <ArticleId IdType="pii">PCOMPBIOL-D-16-00387</ArticleId>
            <ArticleId IdType="pmc">PMC5113863</ArticleId>
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</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="In-Data-Review" Owner="NLM">
        <PMID Version="1">28193880</PMID>
        <DateCreated>
            <Year>2017</Year>
            <Month>02</Month>
            <Day>14</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Print-Electronic">
            <Journal>
                <ISSN IssnType="Electronic">1091-6490</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>114</Volume>
                    <Issue>9</Issue>
                    <PubDate>
                        <Year>2017</Year>
                        <Month>Feb</Month>
                        <Day>28</Day>
                    </PubDate>
                </JournalIssue>
                <Title>Proceedings of the National Academy of Sciences of the United States of America</Title>
                <ISOAbbreviation>Proc. Natl. Acad. Sci. U.S.A.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Spatial and temporal dynamics of superspreading events in the 2014-2015 West Africa Ebola epidemic.</ArticleTitle>
            <Pagination>
                <MedlinePgn>2337-2342</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1073/pnas.1614595114</ELocationID>
            <Abstract>
                <AbstractText>The unprecedented scale of the Ebola outbreak in Western Africa (2014-2015) has prompted an explosion of efforts to understand the transmission dynamics of the virus and to analyze the performance of possible containment strategies. Models have focused primarily on the reproductive numbers of the disease that represent the average number of secondary infections produced by a random infectious individual. However, these population-level estimates may conflate important systematic variation in the number of cases generated by infected individuals, particularly found in spatially localized transmission and superspreading events. Although superspreading features prominently in first-hand narratives of Ebola transmission, its dynamics have not been systematically characterized, hindering refinements of future epidemic predictions and explorations of targeted interventions. We used Bayesian model inference to integrate individual-level spatial information with other epidemiological data of community-based (undetected within clinical-care systems) cases and to explicitly infer distribution of the cases generated by each infected individual. Our results show that superspreaders play a key role in sustaining onward transmission of the epidemic, and they are responsible for a significant proportion ([Formula: see text]61%) of the infections. Our results also suggest age as a key demographic predictor for superspreading. We also show that community-based cases may have progressed more rapidly than those notified within clinical-care systems, and most transmission events occurred in a relatively short distance (with median value of 2.51 km). Our results stress the importance of characterizing superspreading of Ebola, enhance our current understanding of its spatiotemporal dynamics, and highlight the potential importance of targeted control measures.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Lau</LastName>
                    <ForeName>Max S Y</ForeName>
                    <Initials>MS</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544; msylau@princeton.edu.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Dalziel</LastName>
                    <ForeName>Benjamin Douglas</ForeName>
                    <Initials>BD</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Integrative Biology, Oregon State University, Corvallis, OR 97331.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Mathematics, Oregon State University, Corvallis, OR 97331.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Funk</LastName>
                    <ForeName>Sebastian</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London WC1E 7HT, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>McClelland</LastName>
                    <ForeName>Amanda</ForeName>
                    <Initials>A</Initials>
                    <AffiliationInfo>
                        <Affiliation>International Federation of Red Cross and Red Crescent Societies, CH-1211 Geneva 19, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Tiffany</LastName>
                    <ForeName>Amanda</ForeName>
                    <Initials>A</Initials>
                    <AffiliationInfo>
                        <Affiliation>Epicentre, CH-1211 Geneva 6, Switzerland.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Riley</LastName>
                    <ForeName>Steven</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Medical Research Council Centre for Outbreak Analysis and Modelling, Department Infectious Disease Epidemiology, Imperial College London, London SW7 2AZ, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Metcalf</LastName>
                    <ForeName>C Jessica E</ForeName>
                    <Initials>CJ</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Grenfell</LastName>
                    <ForeName>Bryan T</ForeName>
                    <Initials>BT</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Fogarty International Center, National Institutes of Health, Bethesda, MD 20892.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>MR/J008761/1</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>P2C HD047879</GrantID>
                    <Acronym>HD</Acronym>
                    <Agency>NICHD NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2017</Year>
                <Month>02</Month>
                <Day>13</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>Proc Natl Acad Sci U S A</MedlineTA>
            <NlmUniqueID>7505876</NlmUniqueID>
            <ISSNLinking>0027-8424</ISSNLinking>
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        <KeywordList Owner="NOTNLM">
            <Keyword MajorTopicYN="N">Bayesian inference</Keyword>
            <Keyword MajorTopicYN="N">Ebola</Keyword>
            <Keyword MajorTopicYN="N">offspring distribution</Keyword>
            <Keyword MajorTopicYN="N">superspreading</Keyword>
        </KeywordList>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2017</Year>
                <Month>2</Month>
                <Day>15</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="medline">
                <Year>2017</Year>
                <Month>2</Month>
                <Day>15</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="entrez">
                <Year>2017</Year>
                <Month>2</Month>
                <Day>15</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
        </History>
        <PublicationStatus>ppublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28193880</ArticleId>
            <ArticleId IdType="pii">1614595114</ArticleId>
            <ArticleId IdType="doi">10.1073/pnas.1614595114</ArticleId>
            <ArticleId IdType="pmc">PMC5338479</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">27271655</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>06</Month>
            <Day>09</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>08</Day>
        </DateRevised>
        <Article PubModel="Electronic">
            <Journal>
                <ISSN IssnType="Electronic">1999-4915</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>8</Volume>
                    <Issue>6</Issue>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>Jun</Month>
                        <Day>03</Day>
                    </PubDate>
                </JournalIssue>
                <Title>Viruses</Title>
                <ISOAbbreviation>Viruses</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Accurate Measurement of the Effects of All Amino-Acid Mutations on Influenza Hemagglutinin.</ArticleTitle>
            <ELocationID EIdType="doi" ValidYN="Y">10.3390/v8060155</ELocationID>
            <ELocationID EIdType="pii" ValidYN="Y">E155</ELocationID>
            <Abstract>
                <AbstractText>Influenza genes evolve mostly via point mutations, and so knowing the effect of every amino-acid mutation provides information about evolutionary paths available to the virus. We and others have combined high-throughput mutagenesis with deep sequencing to estimate the effects of large numbers of mutations to influenza genes. However, these measurements have suffered from substantial experimental noise due to a variety of technical problems, the most prominent of which is bottlenecking during the generation of mutant viruses from plasmids. Here we describe advances that ameliorate these problems, enabling us to measure with greatly improved accuracy and reproducibility the effects of all amino-acid mutations to an H1 influenza hemagglutinin on viral replication in cell culture. The largest improvements come from using a helper virus to reduce bottlenecks when generating viruses from plasmids. Our measurements confirm at much higher resolution the results of previous studies suggesting that antigenic sites on the globular head of hemagglutinin are highly tolerant of mutations. We also show that other regions of hemagglutinin-including the stalk epitopes targeted by broadly neutralizing antibodies-have a much lower inherent capacity to tolerate point mutations. The ability to accurately measure the effects of all influenza mutations should enhance efforts to understand and predict viral evolution.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Doud</LastName>
                    <ForeName>Michael B</ForeName>
                    <Initials>MB</Initials>
                    <AffiliationInfo>
                        <Affiliation>Division of Basic Sciences Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA. mbdoud@gmail.com.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle, WA 98195-5065, USA. mbdoud@gmail.com.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Medical Scientist Training Program, University of Washington, Seattle, WA 98109, USA. mbdoud@gmail.com.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Bloom</LastName>
                    <ForeName>Jesse D</ForeName>
                    <Initials>JD</Initials>
                    <AffiliationInfo>
                        <Affiliation>Division of Basic Sciences Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA. jbloom@fredhutch.org.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Genome Sciences, University of Washington, 3720 15th Ave NE, Seattle, WA 98195-5065, USA. jbloom@fredhutch.org.</Affiliation>
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        <KeywordList Owner="NOTNLM">
            <Keyword MajorTopicYN="N">deep mutational scanning</Keyword>
            <Keyword MajorTopicYN="N">evolution</Keyword>
            <Keyword MajorTopicYN="N">hemagglutinin</Keyword>
            <Keyword MajorTopicYN="N">influenza</Keyword>
            <Keyword MajorTopicYN="N">mutational tolerance</Keyword>
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        <DateCreated>
            <Year>2017</Year>
            <Month>03</Month>
            <Day>13</Day>
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        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>08</Day>
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                    <Volume>13</Volume>
                    <Issue>3</Issue>
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                        <Year>2017</Year>
                        <Month>Mar</Month>
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                <Title>PLoS pathogens</Title>
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            <ArticleTitle>Complete mapping of viral escape from neutralizing antibodies.</ArticleTitle>
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                <MedlinePgn>e1006271</MedlinePgn>
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                <AbstractText>Identifying viral mutations that confer escape from antibodies is crucial for understanding the interplay between immunity and viral evolution. We describe a high-throughput approach to quantify the selection that monoclonal antibodies exert on all single amino-acid mutations to a viral protein. This approach, mutational antigenic profiling, involves creating all replication-competent protein variants of a virus, selecting with antibody, and using deep sequencing to identify enriched mutations. We use mutational antigenic profiling to comprehensively identify mutations that enable influenza virus to escape four monoclonal antibodies targeting hemagglutinin, and validate key findings with neutralization assays. We find remarkable mutation-level idiosyncrasy in antibody escape: for instance, at a single residue targeted by two antibodies, some mutations escape both antibodies while other mutations escape only one or the other. Because mutational antigenic profiling rapidly maps all mutations selected by an antibody, it is useful for elucidating immune specificities and interpreting the antigenic consequences of viral genetic variation.</AbstractText>
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                <Author ValidYN="Y">
                    <LastName>Doud</LastName>
                    <ForeName>Michael B</ForeName>
                    <Initials>MB</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0002-8172-6342</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Medical Scientist Training Program, University of Washington, Seattle, Washington, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Hensley</LastName>
                    <ForeName>Scott E</ForeName>
                    <Initials>SE</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Bloom</LastName>
                    <ForeName>Jesse D</ForeName>
                    <Initials>JD</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0003-1267-3408</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America.</Affiliation>
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            <Language>eng</Language>
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                <Grant>
                    <GrantID>R01 AI108686</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
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                <Grant>
                    <GrantID>R01 AI127893</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>T32 AI083203</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
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                <Grant>
                    <GrantID>R01 AI113047</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
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                <Grant>
                    <GrantID>R01 GM102198</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U54 GM111274</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
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                <PublicationType UI="D016428">Journal Article</PublicationType>
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            <ArticleDate DateType="Electronic">
                <Year>2017</Year>
                <Month>03</Month>
                <Day>13</Day>
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            <Country>United States</Country>
            <MedlineTA>PLoS Pathog</MedlineTA>
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    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="received">
                <Year>2017</Year>
                <Month>01</Month>
                <Day>20</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2017</Year>
                <Month>03</Month>
                <Day>06</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="revised">
                <Year>2017</Year>
                <Month>03</Month>
                <Day>23</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2017</Year>
                <Month>3</Month>
                <Day>14</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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            <PubMedPubDate PubStatus="medline">
                <Year>2017</Year>
                <Month>3</Month>
                <Day>14</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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            <PubMedPubDate PubStatus="entrez">
                <Year>2017</Year>
                <Month>3</Month>
                <Day>14</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
        </History>
        <PublicationStatus>epublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28288189</ArticleId>
            <ArticleId IdType="doi">10.1371/journal.ppat.1006271</ArticleId>
            <ArticleId IdType="pii">PPATHOGENS-D-17-00133</ArticleId>
            <ArticleId IdType="pmc">PMC5363992</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">28095468</PMID>
        <DateCreated>
            <Year>2017</Year>
            <Month>01</Month>
            <Day>17</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Electronic-eCollection">
            <Journal>
                <ISSN IssnType="Electronic">1549-1676</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>14</Volume>
                    <Issue>1</Issue>
                    <PubDate>
                        <Year>2017</Year>
                        <Month>Jan</Month>
                    </PubDate>
                </JournalIssue>
                <Title>PLoS medicine</Title>
                <ISOAbbreviation>PLoS Med.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Evaluating Hospital-Based Surveillance for Outbreak Detection in Bangladesh: Analysis of Healthcare Utilization Data.</ArticleTitle>
            <Pagination>
                <MedlinePgn>e1002218</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pmed.1002218</ELocationID>
            <Abstract>
                <AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">The International Health Regulations outline core requirements to ensure the detection of public health threats of international concern. Assessing the capacity of surveillance systems to detect these threats is crucial for evaluating a country's ability to meet these requirements.</AbstractText>
                <AbstractText Label="METHODS AND FINDINGS" NlmCategory="RESULTS">We propose a framework to evaluate the sensitivity and representativeness of hospital-based surveillance and apply it to severe neurological infectious diseases and fatal respiratory infectious diseases in Bangladesh. We identified cases in selected communities within surveillance hospital catchment areas using key informant and house-to-house surveys and ascertained where cases had sought care. We estimated the probability of surveillance detecting different sized outbreaks by distance from the surveillance hospital and compared characteristics of cases identified in the community and cases attending surveillance hospitals. We estimated that surveillance detected 26% (95% CI 18%-33%) of severe neurological disease cases and 18% (95% CI 16%-21%) of fatal respiratory disease cases residing at 10 km distance from a surveillance hospital. Detection probabilities decreased markedly with distance. The probability of detecting small outbreaks (three cases) dropped below 50% at distances greater than 26 km for severe neurological disease and at distances greater than 7 km for fatal respiratory disease. Characteristics of cases attending surveillance hospitals were largely representative of all cases; however, neurological disease cases aged &lt;5 y or from the lowest socioeconomic group and fatal respiratory disease cases aged ≥60 y were underrepresented. Our estimates of outbreak detection rely on suspected cases that attend a surveillance hospital receiving laboratory confirmation of disease and being reported to the surveillance system. The extent to which this occurs will depend on disease characteristics (e.g., severity and symptom specificity) and surveillance resources.</AbstractText>
                <AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">We present a new approach to evaluating the sensitivity and representativeness of hospital-based surveillance, making it possible to predict its ability to detect emerging threats.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Nikolay</LastName>
                    <ForeName>Birgit</ForeName>
                    <Initials>B</Initials>
                    <AffiliationInfo>
                        <Affiliation>Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Centre National de la Recherche Scientifique, URA3012, Paris, France.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Salje</LastName>
                    <ForeName>Henrik</ForeName>
                    <Initials>H</Initials>
                    <AffiliationInfo>
                        <Affiliation>Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Centre National de la Recherche Scientifique, URA3012, Paris, France.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Sturm-Ramirez</LastName>
                    <ForeName>Katharine</ForeName>
                    <Initials>K</Initials>
                    <AffiliationInfo>
                        <Affiliation>Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Azziz-Baumgartner</LastName>
                    <ForeName>Eduardo</ForeName>
                    <Initials>E</Initials>
                    <AffiliationInfo>
                        <Affiliation>Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Homaira</LastName>
                    <ForeName>Nusrat</ForeName>
                    <Initials>N</Initials>
                    <AffiliationInfo>
                        <Affiliation>Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Discipline of Paediatrics, School of Women's and Children's Health, University of New South Wales, Sydney, New South Wales, Australia.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ahmed</LastName>
                    <ForeName>Makhdum</ForeName>
                    <Initials>M</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0002-0772-8710</Identifier>
                    <AffiliationInfo>
                        <Affiliation>School of Public Health, University of Texas Health Science Center, Houston, Texas, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Iuliano</LastName>
                    <ForeName>A Danielle</ForeName>
                    <Initials>AD</Initials>
                    <AffiliationInfo>
                        <Affiliation>Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Paul</LastName>
                    <ForeName>Repon C</ForeName>
                    <Initials>RC</Initials>
                    <AffiliationInfo>
                        <Affiliation>Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>School of Public Health and Community Medicine, University of New South Wales Australia, Sydney, New South Wales, Australia.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Rahman</LastName>
                    <ForeName>Mahmudur</ForeName>
                    <Initials>M</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0002-3698-4852</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Institute of Epidemiology, Disease Control and Research, Dhaka, Bangladesh.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Hossain</LastName>
                    <ForeName>M Jahangir</ForeName>
                    <Initials>MJ</Initials>
                    <AffiliationInfo>
                        <Affiliation>Medical Research Council Unit The Gambia, Banjul, The Gambia.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Luby</LastName>
                    <ForeName>Stephen P</ForeName>
                    <Initials>SP</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0001-5385-899X</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Infectious Diseases Division, Stanford University, Stanford, California, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cauchemez</LastName>
                    <ForeName>Simon</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Centre National de la Recherche Scientifique, URA3012, Paris, France.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Gurley</LastName>
                    <ForeName>Emily S</ForeName>
                    <Initials>ES</Initials>
                    <AffiliationInfo>
                        <Affiliation>Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2017</Year>
                <Month>01</Month>
                <Day>17</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>PLoS Med</MedlineTA>
            <NlmUniqueID>101231360</NlmUniqueID>
            <ISSNLinking>1549-1277</ISSNLinking>
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    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="received">
                <Year>2016</Year>
                <Month>07</Month>
                <Day>22</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>09</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="entrez">
                <Year>2017</Year>
                <Month>1</Month>
                <Day>18</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2017</Year>
                <Month>1</Month>
                <Day>18</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="medline">
                <Year>2017</Year>
                <Month>1</Month>
                <Day>18</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
        </History>
        <PublicationStatus>epublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28095468</ArticleId>
            <ArticleId IdType="doi">10.1371/journal.pmed.1002218</ArticleId>
            <ArticleId IdType="pii">PMEDICINE-D-16-02359</ArticleId>
            <ArticleId IdType="pmc">PMC5240927</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">27406695</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>08</Month>
            <Day>27</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Print-Electronic">
            <Journal>
                <ISSN IssnType="Electronic">1750-2659</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>10</Volume>
                    <Issue>6</Issue>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>Nov</Month>
                    </PubDate>
                </JournalIssue>
                <Title>Influenza and other respiratory viruses</Title>
                <ISOAbbreviation>Influenza Other Respir Viruses</ISOAbbreviation>
            </Journal>
            <ArticleTitle>A comparison of hemagglutination inhibition and neutralization assays for characterizing immunity to seasonal influenza A.</ArticleTitle>
            <Pagination>
                <MedlinePgn>518-524</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1111/irv.12408</ELocationID>
            <Abstract>
                <AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Serum antibody to influenza can be used to identify past exposure and measure current immune status. The two most common methods for measuring this are the hemagglutination inhibition assay (HI) and the viral neutralization assay (NT), which have not been systematically compared for a large number of influenza viruses.</AbstractText>
                <AbstractText Label="METHODS" NlmCategory="METHODS">A total of 151 study participants from near Guangzhou, China, were enrolled in 2009 and provided serum. HI and NT assays were performed for 12 historic and recently circulating strains of seasonal influenza A. We compared titers using Spearman correlation and fit models to predict NT using HI results.</AbstractText>
                <AbstractText Label="RESULTS" NlmCategory="RESULTS">We observed high positive mean correlation between HI and NT assays (Spearman's rank correlation, ρ=.86) across all strains. Correlation was highest within subtypes and within close proximity in time. Overall, an HI=20 corresponded to NT=10, and HI=40 corresponded to NT=20. Linear regression of log(NT) on log(HI) was statistically significant, with age modifying this relationship. Strain-specific area under a curve (AUC) indicated good accuracy (&gt;80%) for predicting NT with HI.</AbstractText>
                <AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">While we found high overall correspondence of titers between NT and HI assays for seasonal influenza A, no exact equivalence between assays could be determined. This was further complicated by correspondence between titers changing with age. These findings support generalized comparison of results between assays and give further support for use of the hemagglutination inhibition assay over the more resource intensive viral neutralization assay for seasonal influenza A, although attention should be given to the effect of age on these assays.</AbstractText>
                <CopyrightInformation>© 2016 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley &amp; Sons Ltd.</CopyrightInformation>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Truelove</LastName>
                    <ForeName>Shaun</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Zhu</LastName>
                    <ForeName>Huachen</ForeName>
                    <Initials>H</Initials>
                    <AffiliationInfo>
                        <Affiliation>State Key Laboratory of Emerging Infectious Diseases, The University of Hong Kong, Hong Kong SAR, China.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Lessler</LastName>
                    <ForeName>Justin</ForeName>
                    <Initials>J</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Riley</LastName>
                    <ForeName>Steven</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>School of Public Health, Imperial College, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Read</LastName>
                    <ForeName>Jonathan M</ForeName>
                    <Initials>JM</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology and Public Health, Institute of Infection and Global Health, University of Liverpool, Neston, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Wang</LastName>
                    <ForeName>Shuying</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Guangzhou No. 12 Hospital, Guangzhou, Guangdong, China.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kwok</LastName>
                    <ForeName>Kin On</ForeName>
                    <Initials>KO</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Guan</LastName>
                    <ForeName>Yi</ForeName>
                    <Initials>Y</Initials>
                    <AffiliationInfo>
                        <Affiliation>State Key Laboratory of Emerging Infectious Diseases, The University of Hong Kong, Hong Kong SAR, China.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Jiang</LastName>
                    <ForeName>Chao Qiang</ForeName>
                    <Initials>CQ</Initials>
                    <AffiliationInfo>
                        <Affiliation>Guangzhou No. 12 Hospital, Guangzhou, Guangdong, China.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cummings</LastName>
                    <ForeName>Derek A T</ForeName>
                    <Initials>DA</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. datc@ufl.edu.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Biology, University of Florida, Gainesville, FL, USA. datc@ufl.edu.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA. datc@ufl.edu.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>MR/J008761/1</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>R01 TW008246</GrantID>
                    <Acronym>TW</Acronym>
                    <Agency>FIC NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>08</Month>
                <Day>27</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>England</Country>
            <MedlineTA>Influenza Other Respir Viruses</MedlineTA>
            <NlmUniqueID>101304007</NlmUniqueID>
            <ISSNLinking>1750-2640</ISSNLinking>
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        </CommentsCorrectionsList>
        <KeywordList Owner="NOTNLM">
            <Keyword MajorTopicYN="N">cross-protection</Keyword>
            <Keyword MajorTopicYN="N">hemagglutination inhibition test</Keyword>
            <Keyword MajorTopicYN="N">immunity</Keyword>
            <Keyword MajorTopicYN="N">influenza</Keyword>
            <Keyword MajorTopicYN="N">microneutralization test</Keyword>
            <Keyword MajorTopicYN="N">neutralization test</Keyword>
        </KeywordList>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="accepted">
                <Year>2016</Year>
                <Month>06</Month>
                <Day>29</Day>
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                <Year>2016</Year>
                <Month>7</Month>
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                <Hour>6</Hour>
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            <ArticleId IdType="pubmed">27406695</ArticleId>
            <ArticleId IdType="doi">10.1111/irv.12408</ArticleId>
            <ArticleId IdType="pmc">PMC5059953</ArticleId>
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<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">27018830</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>05</Month>
            <Day>04</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>03</Month>
            <Day>21</Day>
        </DateRevised>
        <Article PubModel="Print">
            <Journal>
                <ISSN IssnType="Electronic">2168-6211</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>170</Volume>
                    <Issue>5</Issue>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>May</Month>
                        <Day>01</Day>
                    </PubDate>
                </JournalIssue>
                <Title>JAMA pediatrics</Title>
                <ISOAbbreviation>JAMA Pediatr</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Epidemiological and Economic Effects of Priming With the Whole-Cell Bordetella pertussis Vaccine.</ArticleTitle>
            <Pagination>
                <MedlinePgn>459-65</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1001/jamapediatrics.2016.0047</ELocationID>
            <Abstract>
                <AbstractText Label="IMPORTANCE" NlmCategory="OBJECTIVE">Current acellular pertussis vaccines may not protect against transmission of Bordetella pertussis.</AbstractText>
                <AbstractText Label="OBJECTIVE" NlmCategory="OBJECTIVE">To assess whether a priming dose of whole-cell pertussis (wP) vaccine is cost-effective at reducing pertussis infection in infants.</AbstractText>
                <AbstractText Label="DESIGN, SETTING, AND PARTICIPANTS" NlmCategory="METHODS">Mathematical model of pertussis transmission fit to US incidence data in a simulation of the US population. In this simulation study conducted from June 2014 to May 2015, the population was divided into 9 age groups corresponding to the current pertussis vaccination schedule and fit to 2012 pertussis incidence.</AbstractText>
                <AbstractText Label="INTERVENTIONS" NlmCategory="METHODS">Inclusion of a priming dose of wP vaccine into the current acellular pertussis vaccination schedule.</AbstractText>
                <AbstractText Label="MAIN OUTCOMES AND MEASURES" NlmCategory="METHODS">Reductions in symptomatic pertussis incidence by age group, increases in wP vaccine-related adverse effects, and quality-adjusted life-years owing to changing vaccine schedule.</AbstractText>
                <AbstractText Label="RESULTS" NlmCategory="RESULTS">Switching to a wP-priming vaccination strategy could reduce whooping cough incidence by up to 95% (95% CI, 91-98), including 96% (95% CI, 92-98) fewer infections in neonates. Although there may be an increase in the number of vaccine adverse effects, we nonetheless estimate a 95% reduction in quality-adjusted life-years lost with a switch to the combined strategy and a cost reduction of 94% (95% CI, 91-97), saving more than $142 million annually.</AbstractText>
                <AbstractText Label="CONCLUSIONS AND RELEVANCE" NlmCategory="CONCLUSIONS">Our results suggest that an alternative vaccination schedule including 1 dose of wP vaccine may be highly cost-effective and ethically preferred until next-generation pertussis vaccines become available.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>DeAngelis</LastName>
                    <ForeName>Haedi</ForeName>
                    <Initials>H</Initials>
                    <AffiliationInfo>
                        <Affiliation>New Mexico State University, Las Cruces.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Scarpino</LastName>
                    <ForeName>Samuel V</ForeName>
                    <Initials>SV</Initials>
                    <AffiliationInfo>
                        <Affiliation>Santa Fe Institute, Santa Fe, New Mexico.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Fitzpatrick</LastName>
                    <ForeName>Meagan C</ForeName>
                    <Initials>MC</Initials>
                    <AffiliationInfo>
                        <Affiliation>Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, Connecticut.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Galvani</LastName>
                    <ForeName>Alison P</ForeName>
                    <Initials>AP</Initials>
                    <AffiliationInfo>
                        <Affiliation>Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, Connecticut4Yale Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Althouse</LastName>
                    <ForeName>Benjamin M</ForeName>
                    <Initials>BM</Initials>
                    <AffiliationInfo>
                        <Affiliation>New Mexico State University, Las Cruces2Santa Fe Institute, Santa Fe, New Mexico.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>U01 GM087719</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>JAMA Pediatr</MedlineTA>
            <NlmUniqueID>101589544</NlmUniqueID>
            <ISSNLinking>2168-6203</ISSNLinking>
        </MedlineJournalInfo>
        <CitationSubset>AIM</CitationSubset>
        <CitationSubset>IM</CitationSubset>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="entrez">
                <Year>2016</Year>
                <Month>3</Month>
                <Day>29</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2016</Year>
                <Month>3</Month>
                <Day>29</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
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                <Year>2016</Year>
                <Month>3</Month>
                <Day>29</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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        </History>
        <PublicationStatus>ppublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">27018830</ArticleId>
            <ArticleId IdType="pii">2505542</ArticleId>
            <ArticleId IdType="doi">10.1001/jamapediatrics.2016.0047</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="In-Data-Review" Owner="NLM">
        <PMID Version="1">28239233</PMID>
        <DateCreated>
            <Year>2017</Year>
            <Month>02</Month>
            <Day>27</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>15</Day>
        </DateRevised>
        <Article PubModel="Print-Electronic">
            <Journal>
                <ISSN IssnType="Print">0883-4237</ISSN>
                <JournalIssue CitedMedium="Print">
                    <Volume>31</Volume>
                    <Issue>3</Issue>
                    <PubDate>
                        <Year>2016</Year>
                    </PubDate>
                </JournalIssue>
                <Title>Statistical science : a review journal of the Institute of Mathematical Statistics</Title>
                <ISOAbbreviation>Stat Sci</ISOAbbreviation>
            </Journal>
            <ArticleTitle>On negative outcome control of unobserved confounding as a generalization of difference-in-differences.</ArticleTitle>
            <Pagination>
                <MedlinePgn>348-361</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1214/16-STS558</ELocationID>
            <Abstract>
                <AbstractText>The difference-in-differences (DID) approach is a well known strategy for estimating the effect of an exposure in the presence of unobserved confounding. The approach is most commonly used when pre-and post-exposure outcome measurements are available, and one can assume that the association of the unobserved confounder with the outcome is equal in the two exposure groups, and constant over time. Then, one recovers the treatment effect by regressing the change in outcome over time on the exposure. In this paper, we interpret the difference-in-differences as a negative outcome control (NOC) approach. We show that the pre-exposure outcome is a negative control outcome, as it cannot be influenced by the subsequent exposure, and it is affected by both observed and unobserved confounders of the exposure-outcome association of interest. The relation between DID and NOC provides simple conditions under which negative control outcomes can be used to detect and correct for confounding bias. However, for general negative control outcomes, the DID-like assumption may be overly restrictive and rarely credible, because it requires that both the outcome of interest and the control outcome are measured on the same scale. Thus, we present a scale-invariant generalization of the DID that may be used in broader NOC contexts. The proposed approach is demonstrated in simulations and on a Normative Aging Study data set, in which Body Mass Index is used for NOC of the relationship between air pollution and inflammatory outcomes.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Sofer</LastName>
                    <ForeName>Tamar</ForeName>
                    <Initials>T</Initials>
                    <AffiliationInfo>
                        <Affiliation>University of Washington, Harvard T.H. Chan School of Public Health, and Gillings School of Global Public Health, University of North Carolina.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Richardson</LastName>
                    <ForeName>David B</ForeName>
                    <Initials>DB</Initials>
                    <AffiliationInfo>
                        <Affiliation>University of Washington, Harvard T.H. Chan School of Public Health, and Gillings School of Global Public Health, University of North Carolina.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Colicino</LastName>
                    <ForeName>Elena</ForeName>
                    <Initials>E</Initials>
                    <AffiliationInfo>
                        <Affiliation>University of Washington, Harvard T.H. Chan School of Public Health, and Gillings School of Global Public Health, University of North Carolina.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Schwartz</LastName>
                    <ForeName>Joel</ForeName>
                    <Initials>J</Initials>
                    <AffiliationInfo>
                        <Affiliation>University of Washington, Harvard T.H. Chan School of Public Health, and Gillings School of Global Public Health, University of North Carolina.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Tchetgen Tchetgen</LastName>
                    <ForeName>Eric J</ForeName>
                    <Initials>EJ</Initials>
                    <AffiliationInfo>
                        <Affiliation>University of Washington, Harvard T.H. Chan School of Public Health, and Gillings School of Global Public Health, University of North Carolina.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>R01 AI104459</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U54 GM088558</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>09</Month>
                <Day>27</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>Stat Sci</MedlineTA>
            <NlmUniqueID>100962994</NlmUniqueID>
            <ISSNLinking>0883-4237</ISSNLinking>
        </MedlineJournalInfo>
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    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="entrez">
                <Year>2017</Year>
                <Month>2</Month>
                <Day>28</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2017</Year>
                <Month>2</Month>
                <Day>28</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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            <PubMedPubDate PubStatus="medline">
                <Year>2017</Year>
                <Month>2</Month>
                <Day>28</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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        </History>
        <PublicationStatus>ppublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28239233</ArticleId>
            <ArticleId IdType="doi">10.1214/16-STS558</ArticleId>
            <ArticleId IdType="pmc">PMC5322866</ArticleId>
            <ArticleId IdType="mid">NIHMS790916</ArticleId>
        </ArticleIdList>
        <?nihms?>
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<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">28170438</PMID>
        <DateCreated>
            <Year>2017</Year>
            <Month>02</Month>
            <Day>07</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>03</Month>
            <Day>12</Day>
        </DateRevised>
        <Article PubModel="Electronic-eCollection">
            <Journal>
                <ISSN IssnType="Electronic">1553-7374</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>13</Volume>
                    <Issue>2</Issue>
                    <PubDate>
                        <Year>2017</Year>
                        <Month>Feb</Month>
                    </PubDate>
                </JournalIssue>
                <Title>PLoS pathogens</Title>
                <ISOAbbreviation>PLoS Pathog.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>The effective rate of influenza reassortment is limited during human infection.</ArticleTitle>
            <Pagination>
                <MedlinePgn>e1006203</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.ppat.1006203</ELocationID>
            <Abstract>
                <AbstractText>We characterise the evolutionary dynamics of influenza infection described by viral sequence data collected from two challenge studies conducted in human hosts. Viral sequence data were collected at regular intervals from infected hosts. Changes in the sequence data observed across time show that the within-host evolution of the virus was driven by the reversion of variants acquired during previous passaging of the virus. Treatment of some patients with oseltamivir on the first day of infection did not lead to the emergence of drug resistance variants in patients. Using an evolutionary model, we inferred the effective rate of reassortment between viral segments, measuring the extent to which randomly chosen viruses within the host exchange genetic material. We find strong evidence that the rate of effective reassortment is low, such that genetic associations between polymorphic loci in different segments are preserved during the course of an infection in a manner not compatible with epistasis. Combining our evidence with that of previous studies we suggest that spatial heterogeneity in the viral population may reduce the extent to which reassortment is observed. Our results do not contradict previous findings of high rates of viral reassortment in vitro and in small animal studies, but indicate that in human hosts the effective rate of reassortment may be substantially more limited.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Sobel Leonard</LastName>
                    <ForeName>Ashley</ForeName>
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            <ArticleId IdType="pii">PPATHOGENS-D-16-02333</ArticleId>
            <ArticleId IdType="pmc">PMC5315410</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">28068413</PMID>
        <DateCreated>
            <Year>2017</Year>
            <Month>01</Month>
            <Day>09</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Electronic-eCollection">
            <Journal>
                <ISSN IssnType="Electronic">1553-7374</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>13</Volume>
                    <Issue>1</Issue>
                    <PubDate>
                        <Year>2017</Year>
                        <Month>Jan</Month>
                    </PubDate>
                </JournalIssue>
                <Title>PLoS pathogens</Title>
                <ISOAbbreviation>PLoS Pathog.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Social and Genetic Networks of HIV-1 Transmission in New York City.</ArticleTitle>
            <Pagination>
                <MedlinePgn>e1006000</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.ppat.1006000</ELocationID>
            <Abstract>
                <AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Sexually transmitted infections spread across contact networks. Partner elicitation and notification are commonly used public health tools to identify, notify, and offer testing to persons linked in these contact networks. For HIV-1, a rapidly evolving pathogen with low per-contact transmission rates, viral genetic sequences are an additional source of data that can be used to infer or refine transmission networks.</AbstractText>
                <AbstractText Label="METHODS AND FINDINGS" NlmCategory="RESULTS">The New York City Department of Health and Mental Hygiene interviews individuals newly diagnosed with HIV and elicits names of sexual and injection drug using partners. By law, the Department of Health also receives HIV sequences when these individuals enter healthcare and their physicians order resistance testing. Our study used both HIV sequence and partner naming data from 1342 HIV-infected persons in New York City between 2006 and 2012 to infer and compare sexual/drug-use named partner and genetic transmission networks. Using these networks, we determined a range of genetic distance thresholds suitable for identifying potential transmission partners. In 48% of cases, named partners were infected with genetically closely related viruses, compatible with but not necessarily representing or implying, direct transmission. Partner pairs linked through the genetic similarity of their HIV sequences were also linked by naming in 53% of cases. Persons who reported high-risk heterosexual contact were more likely to name at least one partner with a genetically similar virus than those reporting their risk as injection drug use or men who have sex with men.</AbstractText>
                <AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">We analyzed an unprecedentedly large and detailed partner tracing and HIV sequence dataset and determined an empirically justified range of genetic distance thresholds for identifying potential transmission partners. We conclude that genetic linkage provides more reliable evidence for identifying potential transmission partners than partner naming, highlighting the importance and complementarity of both epidemiological and molecular genetic surveillance for characterizing regional HIV-1 epidemics.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Wertheim</LastName>
                    <ForeName>Joel O</ForeName>
                    <Initials>JO</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Medicine, University of California San Diego, San Diego, California, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kosakovsky Pond</LastName>
                    <ForeName>Sergei L</ForeName>
                    <Initials>SL</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Medicine, University of California San Diego, San Diego, California, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Forgione</LastName>
                    <ForeName>Lisa A</ForeName>
                    <Initials>LA</Initials>
                    <AffiliationInfo>
                        <Affiliation>New York City Department of Health and Mental Hygiene, New York, New York, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Mehta</LastName>
                    <ForeName>Sanjay R</ForeName>
                    <Initials>SR</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Medicine, University of California San Diego, San Diego, California, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Murrell</LastName>
                    <ForeName>Ben</ForeName>
                    <Initials>B</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Medicine, University of California San Diego, San Diego, California, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Shah</LastName>
                    <ForeName>Sharmila</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>New York City Department of Health and Mental Hygiene, New York, New York, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Smith</LastName>
                    <ForeName>Davey M</ForeName>
                    <Initials>DM</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Medicine, University of California San Diego, San Diego, California, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Veterans Affairs Healthcare System San Diego, San Diego, California, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Scheffler</LastName>
                    <ForeName>Konrad</ForeName>
                    <Initials>K</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Medicine, University of California San Diego, San Diego, California, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Torian</LastName>
                    <ForeName>Lucia V</ForeName>
                    <Initials>LV</Initials>
                    <AffiliationInfo>
                        <Affiliation>New York City Department of Health and Mental Hygiene, New York, New York, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>P30 MH062512</GrantID>
                    <Acronym>MH</Acronym>
                    <Agency>NIMH NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U01 GM110749</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2017</Year>
                <Month>01</Month>
                <Day>09</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>PLoS Pathog</MedlineTA>
            <NlmUniqueID>101238921</NlmUniqueID>
            <ISSNLinking>1553-7366</ISSNLinking>
        </MedlineJournalInfo>
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        <CoiStatement>I have read the journal's policy and the authors of this manuscript have the following competing interests: JOW is a paid consultant for the Centers for Disease Control and Prevention.</CoiStatement>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="received">
                <Year>2016</Year>
                <Month>03</Month>
                <Day>17</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2016</Year>
                <Month>10</Month>
                <Day>14</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="entrez">
                <Year>2017</Year>
                <Month>1</Month>
                <Day>10</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2017</Year>
                <Month>1</Month>
                <Day>10</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="medline">
                <Year>2017</Year>
                <Month>1</Month>
                <Day>10</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
        </History>
        <PublicationStatus>epublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28068413</ArticleId>
            <ArticleId IdType="doi">10.1371/journal.ppat.1006000</ArticleId>
            <ArticleId IdType="pii">PPATHOGENS-D-16-00549</ArticleId>
            <ArticleId IdType="pmc">PMC5221827</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">27153194</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>05</Month>
            <Day>07</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Electronic-eCollection">
            <Journal>
                <ISSN IssnType="Electronic">1932-6203</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>11</Volume>
                    <Issue>5</Issue>
                    <PubDate>
                        <Year>2016</Year>
                    </PubDate>
                </JournalIssue>
                <Title>PloS one</Title>
                <ISOAbbreviation>PLoS ONE</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Individual Correlates of Infectivity of Influenza A Virus Infections in Households.</ArticleTitle>
            <Pagination>
                <MedlinePgn>e0154418</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0154418</ELocationID>
            <Abstract>
                <AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Identifying individual correlates of infectivity of influenza virus is important for disease control and prevention. Viral shedding is used as a proxy measure of infectivity in many studies. However, the evidence for this is limited.</AbstractText>
                <AbstractText Label="METHODS" NlmCategory="METHODS">In a detailed study of influenza virus transmission within households in 2008-12, we recruited index cases with confirmed influenza infection from outpatient clinics, and followed up their household contacts for 7-10 days to identify secondary infections. We used individual-based hazard models to characterize the relationship between individual viral shedding and individual infectivity.</AbstractText>
                <AbstractText Label="RESULTS" NlmCategory="RESULTS">We analyzed 386 households with 1147 household contacts. Index cases were separated into 3 groups according to their estimated level of viral shedding at symptom onset. We did not find a statistically significant association of virus shedding with transmission. Index cases in medium and higher viral shedding groups were estimated to have 21% (95% CI: -29%, 113%) and 44% (CI: -16%, 167%) higher infectivity, compared with those in the lower viral shedding group.</AbstractText>
                <AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Individual viral load measured by RT-PCR in the nose and throat was at most weakly correlated with individual infectivity in households. Other correlates of infectivity should be examined in future studies.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Tsang</LastName>
                    <ForeName>Tim K</ForeName>
                    <Initials>TK</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Fang</LastName>
                    <ForeName>Vicky J</ForeName>
                    <Initials>VJ</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Chan</LastName>
                    <ForeName>Kwok-Hung</ForeName>
                    <Initials>KH</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ip</LastName>
                    <ForeName>Dennis K M</ForeName>
                    <Initials>DK</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Leung</LastName>
                    <ForeName>Gabriel M</ForeName>
                    <Initials>GM</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Peiris</LastName>
                    <ForeName>J S Malik</ForeName>
                    <Initials>JS</Initials>
                    <AffiliationInfo>
                        <Affiliation>WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Centre of Influenza Research, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cowling</LastName>
                    <ForeName>Benjamin J</ForeName>
                    <Initials>BJ</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0002-6297-7154</Identifier>
                    <AffiliationInfo>
                        <Affiliation>WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cauchemez</LastName>
                    <ForeName>Simon</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>05</Month>
                <Day>06</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>PLoS One</MedlineTA>
            <NlmUniqueID>101285081</NlmUniqueID>
            <ISSNLinking>1932-6203</ISSNLinking>
        </MedlineJournalInfo>
        <CitationSubset>IM</CitationSubset>
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                <Year>2015</Year>
                <Month>12</Month>
                <Day>22</Day>
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                <Year>2016</Year>
                <Month>04</Month>
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        <PMID Version="1">27213681</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>05</Month>
            <Day>24</Day>
        </DateCreated>
        <DateCompleted>
            <Year>2017</Year>
            <Month>03</Month>
            <Day>20</Day>
        </DateCompleted>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Electronic-eCollection">
            <Journal>
                <ISSN IssnType="Electronic">1553-7358</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>12</Volume>
                    <Issue>5</Issue>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>05</Month>
                    </PubDate>
                </JournalIssue>
                <Title>PLoS computational biology</Title>
                <ISOAbbreviation>PLoS Comput. Biol.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Modelling Virus and Antibody Dynamics during Dengue Virus Infection Suggests a Role for Antibody in Virus Clearance.</ArticleTitle>
            <Pagination>
                <MedlinePgn>e1004951</MedlinePgn>
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            <ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pcbi.1004951</ELocationID>
            <Abstract>
                <AbstractText>Dengue is an infection of increasing global importance, yet uncertainty remains regarding critical aspects of its virology, immunology and epidemiology. One unanswered question is how infection is controlled and cleared during a dengue infection. Antibody is thought to play a role, but little past work has examined the kinetics of both virus and antibody during natural infections. We present data on multiple virus and antibody titres measurements recorded sequentially during infection from 53 Vietnamese dengue patients. We fit mechanistic mathematical models of the dynamics of viral replication and the host immune response to these data. These models fit the data well. The model with antibody removing virus fits the data best, but with a role suggested for ADCC or other infected cell clearance mechanisms. Our analysis therefore shows that the observed viral and antibody kinetics are consistent with antibody playing a key role in controlling viral replication. This work gives quantitative insight into the relationship between antibody levels and the efficiency of viral clearance. It will inform the future development of mechanistic models of how vaccines and antivirals might modify the course of natural dengue infection.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Clapham</LastName>
                    <ForeName>Hannah E</ForeName>
                    <Initials>HE</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, DIDE, Imperial College, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Quyen</LastName>
                    <ForeName>Than Ha</ForeName>
                    <Initials>TH</Initials>
                    <Identifier Source="ORCID">0000-0001-5417-2738</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Oxford University Clinical Research Unit-Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kien</LastName>
                    <ForeName>Duong Thi Hue</ForeName>
                    <Initials>DT</Initials>
                    <Identifier Source="ORCID">0000-0002-1573-5697</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Oxford University Clinical Research Unit-Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Dorigatti</LastName>
                    <ForeName>Ilaria</ForeName>
                    <Initials>I</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, DIDE, Imperial College, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Simmons</LastName>
                    <ForeName>Cameron P</ForeName>
                    <Initials>CP</Initials>
                    <AffiliationInfo>
                        <Affiliation>Oxford University Clinical Research Unit-Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Centre for Tropical Medicine, University of Oxford, Oxford, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute, Melbourne, Australia.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ferguson</LastName>
                    <ForeName>Neil M</ForeName>
                    <Initials>NM</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, DIDE, Imperial College, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>MR/K010174/1</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
                <PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
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            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>05</Month>
                <Day>23</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>PLoS Comput Biol</MedlineTA>
            <NlmUniqueID>101238922</NlmUniqueID>
            <ISSNLinking>1553-734X</ISSNLinking>
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            <Chemical>
                <RegistryNumber>0</RegistryNumber>
                <NameOfSubstance UI="D057134">Antibodies, Neutralizing</NameOfSubstance>
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            <Chemical>
                <RegistryNumber>0</RegistryNumber>
                <NameOfSubstance UI="D000914">Antibodies, Viral</NameOfSubstance>
            </Chemical>
            <Chemical>
                <RegistryNumber>0</RegistryNumber>
                <NameOfSubstance UI="D012367">RNA, Viral</NameOfSubstance>
            </Chemical>
        </ChemicalList>
        <CitationSubset>IM</CitationSubset>
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        <MeshHeadingList>
            <MeshHeading>
                <DescriptorName UI="D057134" MajorTopicYN="N">Antibodies, Neutralizing</DescriptorName>
                <QualifierName UI="Q000097" MajorTopicYN="N">blood</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D000914" MajorTopicYN="N">Antibodies, Viral</DescriptorName>
                <QualifierName UI="Q000097" MajorTopicYN="Y">blood</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D000920" MajorTopicYN="N">Antibody-Dependent Cell Cytotoxicity</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D003715" MajorTopicYN="N">Dengue</DescriptorName>
                <QualifierName UI="Q000276" MajorTopicYN="Y">immunology</QualifierName>
                <QualifierName UI="Q000821" MajorTopicYN="Y">virology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D003716" MajorTopicYN="N">Dengue Virus</DescriptorName>
                <QualifierName UI="Q000276" MajorTopicYN="Y">immunology</QualifierName>
                <QualifierName UI="Q000502" MajorTopicYN="N">physiology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D054884" MajorTopicYN="N">Host-Pathogen Interactions</DescriptorName>
                <QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D007700" MajorTopicYN="N">Kinetics</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D008954" MajorTopicYN="N">Models, Biological</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D018448" MajorTopicYN="Y">Models, Immunological</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D012367" MajorTopicYN="N">RNA, Viral</DescriptorName>
                <QualifierName UI="Q000097" MajorTopicYN="N">blood</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D019562" MajorTopicYN="N">Viral Load</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D014779" MajorTopicYN="N">Virus Replication</DescriptorName>
                <QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName>
            </MeshHeading>
        </MeshHeadingList>
        <OtherID Source="NLM">PMC4877086</OtherID>
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                <Year>2015</Year>
                <Month>10</Month>
                <Day>06</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2016</Year>
                <Month>04</Month>
                <Day>29</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="entrez">
                <Year>2016</Year>
                <Month>5</Month>
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                <Hour>6</Hour>
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                <Day>24</Day>
                <Hour>6</Hour>
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                <Month>3</Month>
                <Day>21</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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        <PublicationStatus>epublish</PublicationStatus>
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            <ArticleId IdType="doi">10.1371/journal.pcbi.1004951</ArticleId>
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        <PMID Version="1">27834632</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>11</Month>
            <Day>11</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Electronic">
            <Journal>
                <ISSN IssnType="Electronic">2050-084X</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>5</Volume>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>Nov</Month>
                        <Day>11</Day>
                    </PubDate>
                </JournalIssue>
                <Title>eLife</Title>
                <ISOAbbreviation>Elife</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Viral factors in influenza pandemic risk assessment.</ArticleTitle>
            <ELocationID EIdType="doi" ValidYN="Y">10.7554/eLife.18491</ELocationID>
            <ELocationID EIdType="pii" ValidYN="Y">e18491</ELocationID>
            <Abstract>
                <AbstractText>The threat of an influenza A virus pandemic stems from continual virus spillovers from reservoir species, a tiny fraction of which spark sustained transmission in humans. To date, no pandemic emergence of a new influenza strain has been preceded by detection of a closely related precursor in an animal or human. Nonetheless, influenza surveillance efforts are expanding, prompting a need for tools to assess the pandemic risk posed by a detected virus. The goal would be to use genetic sequence and/or biological assays of viral traits to identify those non-human influenza viruses with the greatest risk of evolving into pandemic threats, and/or to understand drivers of such evolution, to prioritize pandemic prevention or response measures. We describe such efforts, identify progress and ongoing challenges, and discuss three specific traits of influenza viruses (hemagglutinin receptor binding specificity, hemagglutinin pH of activation, and polymerase complex efficiency) that contribute to pandemic risk.</AbstractText>
            </Abstract>
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                    <LastName>Lipsitch</LastName>
                    <ForeName>Marc</ForeName>
                    <Initials>M</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0003-1504-9213</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Center for Communicable Disease Dynamics, Harvard T. H Chan School of Public Health, Boston, United States.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, United States.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, United States.</Affiliation>
                    </AffiliationInfo>
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                    <LastName>Barclay</LastName>
                    <ForeName>Wendy</ForeName>
                    <Initials>W</Initials>
                    <AffiliationInfo>
                        <Affiliation>Division of Infectious Disease, Faculty of Medicine, Imperial College, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Raman</LastName>
                    <ForeName>Rahul</ForeName>
                    <Initials>R</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Biological Engineering, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, United States.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Russell</LastName>
                    <ForeName>Charles J</ForeName>
                    <Initials>CJ</Initials>
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                        <Affiliation>Department of Infectious Diseases, St. Jude Children's Research Hospital, Memphis, United States.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Belser</LastName>
                    <ForeName>Jessica A</ForeName>
                    <Initials>JA</Initials>
                    <AffiliationInfo>
                        <Affiliation>Centers for Disease Control and Prevention, Atlanta, United States.</Affiliation>
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                </Author>
                <Author ValidYN="Y">
                    <LastName>Cobey</LastName>
                    <ForeName>Sarah</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Ecology and Evolutionary Biology, University of Chicago, Chicago, United States.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kasson</LastName>
                    <ForeName>Peter M</ForeName>
                    <Initials>PM</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Biomedical Engineering, University of Virginia, Charlottesville, United States.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, United States.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Lloyd-Smith</LastName>
                    <ForeName>James O</ForeName>
                    <Initials>JO</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0001-7941-502X</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Fogarty International Center, National Institutes of Health, Bethesda, United States.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Maurer-Stroh</LastName>
                    <ForeName>Sebastian</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Bioinformatics Institute, Agency for Science Technology and Research, Singapore, Singapore.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>National Public Health Laboratory, Communicable Diseases Division, Ministry of Health, Singapore, Singapore.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Riley</LastName>
                    <ForeName>Steven</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, School of Public Health, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Beauchemin</LastName>
                    <ForeName>Catherine Aa</ForeName>
                    <Initials>CA</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0003-0599-0069</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Department of Physics, Ryerson University, Toronto, Canada.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Bedford</LastName>
                    <ForeName>Trevor</ForeName>
                    <Initials>T</Initials>
                    <AffiliationInfo>
                        <Affiliation>Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Friedrich</LastName>
                    <ForeName>Thomas C</ForeName>
                    <Initials>TC</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Pathobiological Sciences, University of Wisconsin School of Veterinary Medicine, Madison, United States.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Handel</LastName>
                    <ForeName>Andreas</ForeName>
                    <Initials>A</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0002-4622-1146</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, United States.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Herfst</LastName>
                    <ForeName>Sander</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Viroscience, Erasmus Medical Center, Rotterdam, Netherlands.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Murcia</LastName>
                    <ForeName>Pablo R</ForeName>
                    <Initials>PR</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0002-4352-394X</Identifier>
                    <AffiliationInfo>
                        <Affiliation>MRC-University of Glasgow Centre For Virus Research, Glasgow, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Roche</LastName>
                    <ForeName>Benjamin</ForeName>
                    <Initials>B</Initials>
                    <AffiliationInfo>
                        <Affiliation>IRD/UPMC UMMISCO, Montpellier, France.</Affiliation>
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                </Author>
                <Author ValidYN="Y">
                    <LastName>Wilke</LastName>
                    <ForeName>Claus O</ForeName>
                    <Initials>CO</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0002-7470-9261</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Center for Computational Biology and Bioinformatics, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, United States.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Integrative Biology, The University of Texas at Austin, Austin, United States.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Russell</LastName>
                    <ForeName>Colin A</ForeName>
                    <Initials>CA</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0002-2113-162X</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>G0801822</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>MR/J008761/1</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>MR/K010174/1</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>U19 AI117891</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>R01 GM088344</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>11</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>England</Country>
            <MedlineTA>Elife</MedlineTA>
            <NlmUniqueID>101579614</NlmUniqueID>
            <ISSNLinking>2050-084X</ISSNLinking>
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        <KeywordList Owner="NOTNLM">
            <Keyword MajorTopicYN="N">epidemiology</Keyword>
            <Keyword MajorTopicYN="N">global health</Keyword>
            <Keyword MajorTopicYN="N">human</Keyword>
            <Keyword MajorTopicYN="N">infectious disease</Keyword>
            <Keyword MajorTopicYN="N">influenza A</Keyword>
            <Keyword MajorTopicYN="N">microbiology</Keyword>
            <Keyword MajorTopicYN="N">pandemic</Keyword>
            <Keyword MajorTopicYN="N">risk prediction</Keyword>
            <Keyword MajorTopicYN="N">virus</Keyword>
        </KeywordList>
        <CoiStatement>ML: Reports the following financial disclosures for topics unrelated to this manuscript: consulting income from Pfizer and Affinivax (both donated to charity) and research funding from Pfizer and PATH Vaccine Solutions. These entities had no role in the preparation of this work or in the decision to submit the work for publication. Reviewing editor for eLife. CAR: During the prepartion of this manuscript, CAAB received funding through a research contract with AstraZeneca Inc., but for distinct, unrelated research. AstraZeneca Inc. had no role in the preparation of this work or in the decision to submit the work for publication. The other authors declare that no competing interests exist.</CoiStatement>
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                <Year>2016</Year>
                <Month>06</Month>
                <Day>09</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>03</Day>
            </PubMedPubDate>
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                <Year>2016</Year>
                <Month>11</Month>
                <Day>12</Day>
                <Hour>6</Hour>
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                <Year>2016</Year>
                <Month>11</Month>
                <Day>12</Day>
                <Hour>6</Hour>
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                <Month>11</Month>
                <Day>12</Day>
                <Hour>6</Hour>
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            <ArticleId IdType="pubmed">27834632</ArticleId>
            <ArticleId IdType="doi">10.7554/eLife.18491</ArticleId>
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        <PMID Version="1">28017559</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>12</Month>
            <Day>26</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Print-Electronic">
            <Journal>
                <ISSN IssnType="Electronic">1474-4457</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>17</Volume>
                    <Issue>3</Issue>
                    <PubDate>
                        <Year>2017</Year>
                        <Month>Mar</Month>
                    </PubDate>
                </JournalIssue>
                <Title>The Lancet. Infectious diseases</Title>
                <ISOAbbreviation>Lancet Infect Dis</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015-16: a modelling study.</ArticleTitle>
            <Pagination>
                <MedlinePgn>330-338</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="pii" ValidYN="Y">S1473-3099(16)30513-8</ELocationID>
            <ELocationID EIdType="doi" ValidYN="Y">10.1016/S1473-3099(16)30513-8</ELocationID>
            <Abstract>
                <AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock.</AbstractText>
                <AbstractText Label="METHODS" NlmCategory="METHODS">We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region.</AbstractText>
                <AbstractText Label="FINDINGS" NlmCategory="RESULTS">The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5-7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34-0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52-0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13-0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92-0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected.</AbstractText>
                <AbstractText Label="INTERPRETATION" NlmCategory="CONCLUSIONS">Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy.</AbstractText>
                <AbstractText Label="FUNDING" NlmCategory="BACKGROUND">Wellcome Trust.</AbstractText>
                <CopyrightInformation>Copyright © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license. Published by Elsevier Ltd.. All rights reserved.</CopyrightInformation>
            </Abstract>
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                <Author ValidYN="Y">
                    <LastName>Kraemer</LastName>
                    <ForeName>Moritz U G</ForeName>
                    <Initials>MU</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Zoology, University of Oxford, Oxford, UK. Electronic address: moritz.kraemer@zoo.ox.ac.uk.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Faria</LastName>
                    <ForeName>Nuno R</ForeName>
                    <Initials>NR</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Zoology, University of Oxford, Oxford, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Reiner</LastName>
                    <ForeName>Robert C</ForeName>
                    <Initials>RC</Initials>
                    <Suffix>Jr</Suffix>
                    <AffiliationInfo>
                        <Affiliation>Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
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                    <LastName>Golding</LastName>
                    <ForeName>Nick</ForeName>
                    <Initials>N</Initials>
                    <AffiliationInfo>
                        <Affiliation>Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK; School of BioSciences, University of Melbourne, Parkville, VIC, Australia.</Affiliation>
                    </AffiliationInfo>
                </Author>
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                    <LastName>Nikolay</LastName>
                    <ForeName>Birgit</ForeName>
                    <Initials>B</Initials>
                    <AffiliationInfo>
                        <Affiliation>Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France; Centre National de la Recherche Scientifique, URA 3012, Paris, France.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Stasse</LastName>
                    <ForeName>Stephanie</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Health Programme, European Commission, International Cooperation and Development, Delegation en RDC, Kinshasa, Democratic Republic of the Congo.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Johansson</LastName>
                    <ForeName>Michael A</ForeName>
                    <Initials>MA</Initials>
                    <AffiliationInfo>
                        <Affiliation>Centers for Disease Control and Prevention, San Juan, PR, USA; Center for Communicable Disease Dynamics, Harvard T H Chan School of Public Health, MA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Salje</LastName>
                    <ForeName>Henrik</ForeName>
                    <Initials>H</Initials>
                    <AffiliationInfo>
                        <Affiliation>Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France; Centre National de la Recherche Scientifique, URA 3012, Paris, France; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Faye</LastName>
                    <ForeName>Ousmane</ForeName>
                    <Initials>O</Initials>
                    <AffiliationInfo>
                        <Affiliation>Arbovirus and Viral Hemorrhagic Fever Unit, Institut Pasteur da Dakar, Dakar, Senegal.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Wint</LastName>
                    <ForeName>G R William</ForeName>
                    <Initials>GR</Initials>
                    <AffiliationInfo>
                        <Affiliation>Environmental Research Group Oxford, Department of Zoology, Oxford, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Niedrig</LastName>
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                    <AffiliationInfo>
                        <Affiliation>Robert Koch Institut, Berlin, Germany.</Affiliation>
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                    <Initials>FM</Initials>
                    <AffiliationInfo>
                        <Affiliation>Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK.</Affiliation>
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                </Author>
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                    <Initials>SC</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Zoology, University of Oxford, Oxford, UK.</Affiliation>
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                </Author>
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                    <LastName>Thompson</LastName>
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                    <Initials>RN</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Zoology, University of Oxford, Oxford, UK.</Affiliation>
                    </AffiliationInfo>
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                    <LastName>Bisanzio</LastName>
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                    <Initials>D</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Zoology, University of Oxford, Oxford, UK.</Affiliation>
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                </Author>
                <Author ValidYN="Y">
                    <LastName>Taveira</LastName>
                    <ForeName>Nuno</ForeName>
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                    <AffiliationInfo>
                        <Affiliation>Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, University of Lisbon, Portugal; Centro de Investigacao Interdisciplinar Egas Moniz, Instituto Superior de Ciencias da Saude Egas Moniz, Caparica, Portugal.</Affiliation>
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                        <Affiliation>Computational Social Science, ETH Zurich, Zurich, Switzerland.</Affiliation>
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                        <Affiliation>Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.</Affiliation>
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            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>095066</GrantID>
                    <Agency>Wellcome Trust</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>R01 LM010812</GrantID>
                    <Acronym>LM</Acronym>
                    <Agency>NLM NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>K01 ES025438</GrantID>
                    <Acronym>ES</Acronym>
                    <Agency>NIEHS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U54 GM088558</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U19 AI089674</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>23</Day>
            </ArticleDate>
        </Article>
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            <Country>United States</Country>
            <MedlineTA>Lancet Infect Dis</MedlineTA>
            <NlmUniqueID>101130150</NlmUniqueID>
            <ISSNLinking>1473-3099</ISSNLinking>
        </MedlineJournalInfo>
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    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="received">
                <Year>2016</Year>
                <Month>09</Month>
                <Day>20</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="revised">
                <Year>2016</Year>
                <Month>10</Month>
                <Day>28</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>09</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>27</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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                <Year>2016</Year>
                <Month>12</Month>
                <Day>27</Day>
                <Hour>6</Hour>
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                <Year>2016</Year>
                <Month>12</Month>
                <Day>27</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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        </History>
        <PublicationStatus>ppublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28017559</ArticleId>
            <ArticleId IdType="pii">S1473-3099(16)30513-8</ArticleId>
            <ArticleId IdType="doi">10.1016/S1473-3099(16)30513-8</ArticleId>
            <ArticleId IdType="pmc">PMC5332542</ArticleId>
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<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">27928091</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>12</Month>
            <Day>08</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Print-Electronic">
            <Journal>
                <ISSN IssnType="Electronic">1476-1645</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>95</Volume>
                    <Issue>6</Issue>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>Dec</Month>
                        <Day>07</Day>
                    </PubDate>
                </JournalIssue>
                <Title>The American journal of tropical medicine and hygiene</Title>
                <ISOAbbreviation>Am. J. Trop. Med. Hyg.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>A Meta-Analysis of Serological Response Associated with Yellow Fever Vaccination.</ArticleTitle>
            <Pagination>
                <MedlinePgn>1435-1439</MedlinePgn>
            </Pagination>
            <Abstract>
                <AbstractText>Despite previous evidence of high level of efficacy, no synthetic metric of yellow fever (YF) vaccine efficacy is currently available. Based on the studies identified in a recent systematic review, we conducted a random-effects meta-analysis of the serological response associated with YF vaccination. Eleven studies conducted between 1965 and 2011 representing 4,868 individual observations were included in the meta-analysis. The pooled estimate of serological response was 97.5% (95% confidence interval [CI] = 82.9-99.7%). There was evidence of between-study heterogeneity (I(2) = 89.1%), but this heterogeneity did not appear to be related to study size, study design, or seroconversion measurement or definition. Pooled estimates were significantly higher (P &lt; 0.0001) among studies conducted in nonendemic settings (98.9%, 95% CI = 98.2-99.4%) than among those conducted in endemic settings (94.2%, 95% CI = 83.8-98.1%). These results provide background information against which to evaluate the efficacy of fractional doses of YF vaccine that may be used in outbreak situations.</AbstractText>
                <CopyrightInformation>© The American Society of Tropical Medicine and Hygiene.</CopyrightInformation>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Jean</LastName>
                    <ForeName>Kévin</ForeName>
                    <Initials>K</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom. k.jean@imperial.ac.uk.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Donnelly</LastName>
                    <ForeName>Christl A</ForeName>
                    <Initials>CA</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ferguson</LastName>
                    <ForeName>Neil M</ForeName>
                    <Initials>NM</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Garske</LastName>
                    <ForeName>Tini</ForeName>
                    <Initials>T</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>MR/K010174/1</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>10</Month>
                <Day>10</Day>
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            <MedlineTA>Am J Trop Med Hyg</MedlineTA>
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    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="received">
                <Year>2016</Year>
                <Month>05</Month>
                <Day>18</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2016</Year>
                <Month>07</Month>
                <Day>29</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="entrez">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>9</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>9</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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            <PubMedPubDate PubStatus="medline">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>9</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
        </History>
        <PublicationStatus>ppublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">27928091</ArticleId>
            <ArticleId IdType="pii">ajtmh.16-0401</ArticleId>
            <ArticleId IdType="doi">10.4269/ajtmh.16-0401</ArticleId>
            <ArticleId IdType="pmc">PMC5154464</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">28346537</PMID>
        <DateCreated>
            <Year>2017</Year>
            <Month>03</Month>
            <Day>27</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>08</Day>
        </DateRevised>
        <Article PubModel="Electronic-eCollection">
            <Journal>
                <ISSN IssnType="Electronic">1553-7374</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>13</Volume>
                    <Issue>3</Issue>
                    <PubDate>
                        <Year>2017</Year>
                        <Month>Mar</Month>
                    </PubDate>
                </JournalIssue>
                <Title>PLoS pathogens</Title>
                <ISOAbbreviation>PLoS Pathog.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Deep mutational scanning identifies sites in influenza nucleoprotein that affect viral inhibition by MxA.</ArticleTitle>
            <Pagination>
                <MedlinePgn>e1006288</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.ppat.1006288</ELocationID>
            <Abstract>
                <AbstractText>The innate-immune restriction factor MxA inhibits influenza replication by targeting the viral nucleoprotein (NP). Human influenza virus is more resistant than avian influenza virus to inhibition by human MxA, and prior work has compared human and avian viral strains to identify amino-acid differences in NP that affect sensitivity to MxA. However, this strategy is limited to identifying sites in NP where mutations that affect MxA sensitivity have fixed during the small number of documented zoonotic transmissions of influenza to humans. Here we use an unbiased deep mutational scanning approach to quantify how all single amino-acid mutations to NP affect MxA sensitivity in the context of replication-competent virus. We both identify new sites in NP where mutations affect MxA resistance and re-identify mutations known to have increased MxA resistance during historical adaptations of influenza to humans. Most of the sites where mutations have the greatest effect are almost completely conserved across all influenza A viruses, and the amino acids at these sites confer relatively high resistance to MxA. These sites cluster in regions of NP that appear to be important for its recognition by MxA. Overall, our work systematically identifies the sites in influenza nucleoprotein where mutations affect sensitivity to MxA. We also demonstrate a powerful new strategy for identifying regions of viral proteins that affect inhibition by host factors.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Ashenberg</LastName>
                    <ForeName>Orr</ForeName>
                    <Initials>O</Initials>
                    <AffiliationInfo>
                        <Affiliation>Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Padmakumar</LastName>
                    <ForeName>Jai</ForeName>
                    <Initials>J</Initials>
                    <AffiliationInfo>
                        <Affiliation>Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Doud</LastName>
                    <ForeName>Michael B</ForeName>
                    <Initials>MB</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0002-8172-6342</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Genome Sciences, University of Washington, Seattle, WA, USA.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Medical Scientist Training Program, University of Washington School of Medicine, Seattle, WA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Bloom</LastName>
                    <ForeName>Jesse D</ForeName>
                    <Initials>JD</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0003-1267-3408</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Genome Sciences, University of Washington, Seattle, WA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>R01 AI127893</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>R01 GM102198</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>T32 AI083203</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U54 GM111274</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2017</Year>
                <Month>03</Month>
                <Day>27</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>PLoS Pathog</MedlineTA>
            <NlmUniqueID>101238921</NlmUniqueID>
            <ISSNLinking>1553-7366</ISSNLinking>
        </MedlineJournalInfo>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="received">
                <Year>2016</Year>
                <Month>08</Month>
                <Day>28</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2017</Year>
                <Month>03</Month>
                <Day>10</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="revised">
                <Year>2017</Year>
                <Month>04</Month>
                <Day>06</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2017</Year>
                <Month>3</Month>
                <Day>28</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="medline">
                <Year>2017</Year>
                <Month>3</Month>
                <Day>28</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="entrez">
                <Year>2017</Year>
                <Month>3</Month>
                <Day>28</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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        </History>
        <PublicationStatus>epublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28346537</ArticleId>
            <ArticleId IdType="doi">10.1371/journal.ppat.1006288</ArticleId>
            <ArticleId IdType="pii">PPATHOGENS-D-16-01991</ArticleId>
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    </PubmedData>
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<PubmedArticle>
    <MedlineCitation Status="MEDLINE" Owner="NLM">
        <PMID Version="1">27205899</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>05</Month>
            <Day>21</Day>
        </DateCreated>
        <DateCompleted>
            <Year>2017</Year>
            <Month>03</Month>
            <Day>30</Day>
        </DateCompleted>
        <DateRevised>
            <Year>2017</Year>
            <Month>03</Month>
            <Day>30</Day>
        </DateRevised>
        <Article PubModel="Electronic-eCollection">
            <Journal>
                <ISSN IssnType="Electronic">1935-2735</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>10</Volume>
                    <Issue>5</Issue>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>05</Month>
                    </PubDate>
                </JournalIssue>
                <Title>PLoS neglected tropical diseases</Title>
                <ISOAbbreviation>PLoS Negl Trop Dis</ISOAbbreviation>
            </Journal>
            <ArticleTitle>A Cost-Effectiveness Tool for Informing Policies on Zika Virus Control.</ArticleTitle>
            <Pagination>
                <MedlinePgn>e0004743</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pntd.0004743</ELocationID>
            <Abstract>
                <AbstractText Label="BACKGROUND">As Zika virus continues to spread, decisions regarding resource allocations to control the outbreak underscore the need for a tool to weigh policies according to their cost and the health burden they could avert. For example, to combat the current Zika outbreak the US President requested the allocation of $1.8 billion from Congress in February 2016.</AbstractText>
                <AbstractText Label="METHODOLOGY/PRINCIPAL FINDINGS">Illustrated through an interactive tool, we evaluated how the number of Zika cases averted, the period during pregnancy in which Zika infection poses a risk of microcephaly, and probabilities of microcephaly and Guillain-Barré Syndrome (GBS) impact the cost at which an intervention is cost-effective. From Northeast Brazilian microcephaly incidence data, we estimated the probability of microcephaly in infants born to Zika-infected women (0.49% to 2.10%). We also estimated the probability of GBS arising from Zika infections in Brazil (0.02% to 0.06%) and Colombia (0.08%). We calculated that each microcephaly and GBS case incurs the loss of 29.95 DALYs and 1.25 DALYs per case, as well as direct medical costs for Latin America and the Caribbean of $91,102 and $28,818, respectively. We demonstrated the utility of our cost-effectiveness tool with examples evaluating funding commitments by Costa Rica and Brazil, the US presidential proposal, and the novel approach of genetically modified mosquitoes. Our analyses indicate that the commitments and the proposal are likely to be cost-effective, whereas the cost-effectiveness of genetically modified mosquitoes depends on the country of implementation.</AbstractText>
                <AbstractText Label="CONCLUSIONS/SIGNIFICANCE">Current estimates from our tool suggest that the health burden from microcephaly and GBS warrants substantial expenditures focused on Zika virus control. Our results justify the funding committed in Costa Rica and Brazil and many aspects of the budget outlined in the US president's proposal. As data continue to be collected, new parameter estimates can be customized in real-time within our user-friendly tool to provide updated estimates on cost-effectiveness of interventions and inform policy decisions in country-specific settings.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Alfaro-Murillo</LastName>
                    <ForeName>Jorge A</ForeName>
                    <Initials>JA</Initials>
                    <AffiliationInfo>
                        <Affiliation>Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Parpia</LastName>
                    <ForeName>Alyssa S</ForeName>
                    <Initials>AS</Initials>
                    <Identifier Source="ORCID">0000-0002-9083-1929</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Fitzpatrick</LastName>
                    <ForeName>Meagan C</ForeName>
                    <Initials>MC</Initials>
                    <AffiliationInfo>
                        <Affiliation>Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Tamagnan</LastName>
                    <ForeName>Jules A</ForeName>
                    <Initials>JA</Initials>
                    <AffiliationInfo>
                        <Affiliation>Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Medlock</LastName>
                    <ForeName>Jan</ForeName>
                    <Initials>J</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Biomedical Sciences, College of Veterinary Medicine, Oregon State University, Corvallis, Oregon, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ndeffo-Mbah</LastName>
                    <ForeName>Martial L</ForeName>
                    <Initials>ML</Initials>
                    <AffiliationInfo>
                        <Affiliation>Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Fish</LastName>
                    <ForeName>Durland</ForeName>
                    <Initials>D</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ávila-Agüero</LastName>
                    <ForeName>María L</ForeName>
                    <Initials>ML</Initials>
                    <AffiliationInfo>
                        <Affiliation>Pediatric Infectious Diseases Department, Hospital Nacional de Niños &quot;Dr. Carlos Sáenz Herrera&quot;, San José, Costa Rica.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Marín</LastName>
                    <ForeName>Rodrigo</ForeName>
                    <Initials>R</Initials>
                    <AffiliationInfo>
                        <Affiliation>Programa de Control de Vectores, Ministerio de Salud, San José, Costa Rica.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ko</LastName>
                    <ForeName>Albert I</ForeName>
                    <Initials>AI</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Centro de Pesquisas Gonçalo Moniz, Fundação Oswaldo Cruz, Ministério da Saúde, Salvador, Bahia, Brasil.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Galvani</LastName>
                    <ForeName>Alison P</ForeName>
                    <Initials>AP</Initials>
                    <AffiliationInfo>
                        <Affiliation>Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, United States of America.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>R01 AI121207</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U01 GM087719</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U01 GM105627</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
                <PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>05</Month>
                <Day>20</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>PLoS Negl Trop Dis</MedlineTA>
            <NlmUniqueID>101291488</NlmUniqueID>
            <ISSNLinking>1935-2727</ISSNLinking>
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        </CommentsCorrectionsList>
        <MeshHeadingList>
            <MeshHeading>
                <DescriptorName UI="D000330" MajorTopicYN="N">Aedes</DescriptorName>
                <QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName>
                <QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D030801" MajorTopicYN="N">Animals, Genetically Modified</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D001938" MajorTopicYN="N">Brazil</DescriptorName>
                <QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D017691" MajorTopicYN="N">Caribbean Region</DescriptorName>
                <QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D017281" MajorTopicYN="N">Cost of Illness</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D003362" MajorTopicYN="N">Cost-Benefit Analysis</DescriptorName>
                <QualifierName UI="Q000331" MajorTopicYN="N">legislation &amp; jurisprudence</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D003364" MajorTopicYN="N">Costa Rica</DescriptorName>
                <QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D004196" MajorTopicYN="N">Disease Outbreaks</DescriptorName>
                <QualifierName UI="Q000191" MajorTopicYN="N">economics</QualifierName>
                <QualifierName UI="Q000331" MajorTopicYN="N">legislation &amp; jurisprudence</QualifierName>
                <QualifierName UI="Q000517" MajorTopicYN="N">prevention &amp; control</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D017048" MajorTopicYN="Y">Health Care Costs</DescriptorName>
                <QualifierName UI="Q000331" MajorTopicYN="N">legislation &amp; jurisprudence</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D006291" MajorTopicYN="Y">Health Policy</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D015994" MajorTopicYN="N">Incidence</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D007223" MajorTopicYN="N">Infant</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D008831" MajorTopicYN="N">Microcephaly</DescriptorName>
                <QualifierName UI="Q000209" MajorTopicYN="N">etiology</QualifierName>
                <QualifierName UI="Q000517" MajorTopicYN="N">prevention &amp; control</QualifierName>
                <QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D011247" MajorTopicYN="N">Pregnancy</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D011251" MajorTopicYN="N">Pregnancy Complications, Infectious</DescriptorName>
                <QualifierName UI="Q000191" MajorTopicYN="N">economics</QualifierName>
                <QualifierName UI="Q000517" MajorTopicYN="N">prevention &amp; control</QualifierName>
                <QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D000071244" MajorTopicYN="N">Zika Virus</DescriptorName>
                <QualifierName UI="Q000302" MajorTopicYN="N">isolation &amp; purification</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D000071243" MajorTopicYN="N">Zika Virus Infection</DescriptorName>
                <QualifierName UI="Q000191" MajorTopicYN="Y">economics</QualifierName>
                <QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName>
                <QualifierName UI="Q000517" MajorTopicYN="Y">prevention &amp; control</QualifierName>
                <QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName>
            </MeshHeading>
        </MeshHeadingList>
        <OtherID Source="NLM">PMC4874682</OtherID>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="received">
                <Year>2016</Year>
                <Month>02</Month>
                <Day>25</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2016</Year>
                <Month>05</Month>
                <Day>05</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="entrez">
                <Year>2016</Year>
                <Month>5</Month>
                <Day>21</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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                <Year>2016</Year>
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                <Day>21</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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            <PubMedPubDate PubStatus="medline">
                <Year>2017</Year>
                <Month>3</Month>
                <Day>31</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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        </History>
        <PublicationStatus>epublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">27205899</ArticleId>
            <ArticleId IdType="doi">10.1371/journal.pntd.0004743</ArticleId>
            <ArticleId IdType="pii">PNTD-D-16-00331</ArticleId>
            <ArticleId IdType="pmc">PMC4874682</ArticleId>
        </ArticleIdList>
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</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">28322703</PMID>
        <DateCreated>
            <Year>2017</Year>
            <Month>03</Month>
            <Day>21</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>08</Day>
        </DateRevised>
        <Article PubModel="Print">
            <Journal>
                <ISSN IssnType="Electronic">1080-6059</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>23</Volume>
                    <Issue>4</Issue>
                    <PubDate>
                        <Year>2017</Year>
                        <Month>Apr</Month>
                    </PubDate>
                </JournalIssue>
                <Title>Emerging infectious diseases</Title>
                <ISOAbbreviation>Emerging Infect. Dis.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Persistent Arthralgia Associated with Chikungunya Virus Outbreak, US Virgin Islands, December 2014-February 2016.</ArticleTitle>
            <Pagination>
                <MedlinePgn>673-676</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.3201/eid2304.161562</ELocationID>
            <Abstract>
                <AbstractText>After the 2014-2015 outbreak of chikungunya virus in the US Virgin Islands, we compared the prevalence of persistent arthralgia among case-patients and controls. Prevalence was higher in case-patients than controls 6 and 12 months after disease onset. Continued vaccine research to prevent acute illness and long-term sequelae is essential.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Feldstein</LastName>
                    <ForeName>Leora R</ForeName>
                    <Initials>LR</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Rowhani-Rahbar</LastName>
                    <ForeName>Ali</ForeName>
                    <Initials>A</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Staples</LastName>
                    <ForeName>J Erin</ForeName>
                    <Initials>JE</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Weaver</LastName>
                    <ForeName>Marcia R</ForeName>
                    <Initials>MR</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Halloran</LastName>
                    <ForeName>M Elizabeth</ForeName>
                    <Initials>ME</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ellis</LastName>
                    <ForeName>Esther M</ForeName>
                    <Initials>EM</Initials>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>R37 AI032042</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U54 GM111274</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
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                <PublicationType UI="D016428">Journal Article</PublicationType>
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            <Country>United States</Country>
            <MedlineTA>Emerg Infect Dis</MedlineTA>
            <NlmUniqueID>9508155</NlmUniqueID>
            <ISSNLinking>1080-6040</ISSNLinking>
        </MedlineJournalInfo>
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        <KeywordList Owner="NOTNLM">
            <Keyword MajorTopicYN="N">US Virgin Islands</Keyword>
            <Keyword MajorTopicYN="N">alphavirus</Keyword>
            <Keyword MajorTopicYN="N">chikungunya fever</Keyword>
            <Keyword MajorTopicYN="N">chikungunya virus</Keyword>
            <Keyword MajorTopicYN="N">comparative study</Keyword>
            <Keyword MajorTopicYN="N">long-term sequelae</Keyword>
            <Keyword MajorTopicYN="N">outbreak</Keyword>
            <Keyword MajorTopicYN="N">persistent arthralgia</Keyword>
            <Keyword MajorTopicYN="N">vector-borne infections</Keyword>
            <Keyword MajorTopicYN="N">viruses</Keyword>
        </KeywordList>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="entrez">
                <Year>2017</Year>
                <Month>3</Month>
                <Day>22</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2017</Year>
                <Month>3</Month>
                <Day>23</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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            <PubMedPubDate PubStatus="medline">
                <Year>2017</Year>
                <Month>3</Month>
                <Day>23</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
        </History>
        <PublicationStatus>ppublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28322703</ArticleId>
            <ArticleId IdType="doi">10.3201/eid2304.161562</ArticleId>
            <ArticleId IdType="pmc">PMC5367425</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">28245814</PMID>
        <DateCreated>
            <Year>2017</Year>
            <Month>03</Month>
            <Day>01</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>03</Month>
            <Day>08</Day>
        </DateRevised>
        <Article PubModel="Electronic">
            <Journal>
                <ISSN IssnType="Electronic">1741-7015</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>15</Volume>
                    <Issue>1</Issue>
                    <PubDate>
                        <Year>2017</Year>
                        <Month>Mar</Month>
                        <Day>01</Day>
                    </PubDate>
                </JournalIssue>
                <Title>BMC medicine</Title>
                <ISOAbbreviation>BMC Med</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Perspectives on model forecasts of the 2014-2015 Ebola epidemic in West Africa: lessons and the way forward.</ArticleTitle>
            <Pagination>
                <MedlinePgn>42</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1186/s12916-017-0811-y</ELocationID>
            <Abstract>
                <AbstractText>The unprecedented impact and modeling efforts associated with the 2014-2015 Ebola epidemic in West Africa provides a unique opportunity to document the performances and caveats of forecasting approaches used in near-real time for generating evidence and to guide policy. A number of international academic groups have developed and parameterized mathematical models of disease spread to forecast the trajectory of the outbreak. These modeling efforts often relied on limited epidemiological data to derive key transmission and severity parameters, which are needed to calibrate mechanistic models. Here, we provide a perspective on some of the challenges and lessons drawn from these efforts, focusing on (1) data availability and accuracy of early forecasts; (2) the ability of different models to capture the profile of early growth dynamics in local outbreaks and the importance of reactive behavior changes and case clustering; (3) challenges in forecasting the long-term epidemic impact very early in the outbreak; and (4) ways to move forward. We conclude that rapid availability of aggregated population-level data and detailed information on a subset of transmission chains is crucial to characterize transmission patterns, while ensemble-forecasting approaches could limit the uncertainty of any individual model. We believe that coordinated forecasting efforts, combined with rapid dissemination of disease predictions and underlying epidemiological data in shared online platforms, will be critical in optimizing the response to current and future infectious disease emergencies.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Chowell</LastName>
                    <ForeName>Gerardo</ForeName>
                    <Initials>G</Initials>
                    <AffiliationInfo>
                        <Affiliation>School of Public Health, Georgia State University, Atlanta, GA, USA. gchowell@gsu.edu.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA. gchowell@gsu.edu.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Viboud</LastName>
                    <ForeName>Cécile</ForeName>
                    <Initials>C</Initials>
                    <AffiliationInfo>
                        <Affiliation>Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Simonsen</LastName>
                    <ForeName>Lone</ForeName>
                    <Initials>L</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Public Health, University of Copenhagen, Copenhagen, Denmark.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Global Health, George Washington University, Washington DC, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Merler</LastName>
                    <ForeName>Stefano</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Bruno Kessler Foundation, Trento, Italy.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Vespignani</LastName>
                    <ForeName>Alessandro</ForeName>
                    <Initials>A</Initials>
                    <AffiliationInfo>
                        <Affiliation>Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>U54 GM111274</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016421">Editorial</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2017</Year>
                <Month>03</Month>
                <Day>01</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>England</Country>
            <MedlineTA>BMC Med</MedlineTA>
            <NlmUniqueID>101190723</NlmUniqueID>
            <ISSNLinking>1741-7015</ISSNLinking>
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        <KeywordList Owner="NOTNLM">
            <Keyword MajorTopicYN="N">Data sharing</Keyword>
            <Keyword MajorTopicYN="N">Disease forecast</Keyword>
            <Keyword MajorTopicYN="N">Ebola</Keyword>
            <Keyword MajorTopicYN="N">Epidemic model</Keyword>
            <Keyword MajorTopicYN="N">Exponential growth</Keyword>
            <Keyword MajorTopicYN="N">Lessons learned</Keyword>
            <Keyword MajorTopicYN="N">Polynomial growth</Keyword>
            <Keyword MajorTopicYN="N">Sub-exponential growth</Keyword>
            <Keyword MajorTopicYN="N">West Africa</Keyword>
        </KeywordList>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="received">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>18</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2017</Year>
                <Month>02</Month>
                <Day>07</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="entrez">
                <Year>2017</Year>
                <Month>3</Month>
                <Day>2</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2017</Year>
                <Month>3</Month>
                <Day>2</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="medline">
                <Year>2017</Year>
                <Month>3</Month>
                <Day>2</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
        </History>
        <PublicationStatus>epublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28245814</ArticleId>
            <ArticleId IdType="doi">10.1186/s12916-017-0811-y</ArticleId>
            <ArticleId IdType="pii">10.1186/s12916-017-0811-y</ArticleId>
            <ArticleId IdType="pmc">PMC5331683</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">27399793</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>07</Month>
            <Day>12</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Electronic-eCollection">
            <Journal>
                <ISSN IssnType="Electronic">1935-2735</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>10</Volume>
                    <Issue>7</Issue>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>Jul</Month>
                    </PubDate>
                </JournalIssue>
                <Title>PLoS neglected tropical diseases</Title>
                <ISOAbbreviation>PLoS Negl Trop Dis</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries.</ArticleTitle>
            <Pagination>
                <MedlinePgn>e0004833</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pntd.0004833</ELocationID>
            <Abstract>
                <AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Despite being the most widely distributed mosquito-borne viral infection, estimates of dengue transmission intensity and associated burden remain ambiguous. With advances in the development of novel control measures, obtaining robust estimates of average dengue transmission intensity is key for assessing the burden of disease and the likely impact of interventions.</AbstractText>
                <AbstractText Label="METHODOLOGY/PRINCIPLE FINDINGS" NlmCategory="RESULTS">We estimated the force of infection (λ) and corresponding basic reproduction numbers (R0) by fitting catalytic models to age-stratified incidence data identified from the literature. We compared estimates derived from incidence and seroprevalence data and assessed the level of under-reporting of dengue disease. In addition, we estimated the relative contribution of primary to quaternary infections to the observed burden of dengue disease incidence. The majority of R0 estimates ranged from one to five and the force of infection estimates from incidence data were consistent with those previously estimated from seroprevalence data. The baseline reporting rate (or the probability of detecting a secondary infection) was generally low (&lt;25%) and varied within and between countries.</AbstractText>
                <AbstractText Label="CONCLUSIONS/SIGNIFICANCE" NlmCategory="CONCLUSIONS">As expected, estimates varied widely across and within countries, highlighting the spatio-temporally heterogeneous nature of dengue transmission. Although seroprevalence data provide the maximum information, the incidence models presented in this paper provide a method for estimating dengue transmission intensity from age-stratified incidence data, which will be an important consideration in areas where seroprevalence data are not available.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Imai</LastName>
                    <ForeName>Natsuko</ForeName>
                    <Initials>N</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Dorigatti</LastName>
                    <ForeName>Ilaria</ForeName>
                    <Initials>I</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cauchemez</LastName>
                    <ForeName>Simon</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ferguson</LastName>
                    <ForeName>Neil M</ForeName>
                    <Initials>NM</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>MR/K010174/1</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>07</Month>
                <Day>11</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>PLoS Negl Trop Dis</MedlineTA>
            <NlmUniqueID>101291488</NlmUniqueID>
            <ISSNLinking>1935-2727</ISSNLinking>
        </MedlineJournalInfo>
        <CitationSubset>IM</CitationSubset>
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                <Year>2016</Year>
                <Month>02</Month>
                <Day>23</Day>
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                <Year>2016</Year>
                <Month>06</Month>
                <Day>17</Day>
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                <Year>2016</Year>
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            <ArticleId IdType="doi">10.1371/journal.pntd.0004833</ArticleId>
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        <PMID Version="1">27701113</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>10</Month>
            <Day>04</Day>
        </DateCreated>
        <DateCompleted>
            <Year>2017</Year>
            <Month>03</Month>
            <Day>10</Day>
        </DateCompleted>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Print">
            <Journal>
                <ISSN IssnType="Electronic">1095-9203</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>353</Volume>
                    <Issue>6303</Issue>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>09</Month>
                        <Day>02</Day>
                    </PubDate>
                </JournalIssue>
                <Title>Science (New York, N.Y.)</Title>
                <ISOAbbreviation>Science</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Benefits and risks of the Sanofi-Pasteur dengue vaccine: Modeling optimal deployment.</ArticleTitle>
            <Pagination>
                <MedlinePgn>1033-1036</MedlinePgn>
            </Pagination>
            <Abstract>
                <AbstractText>The first approved dengue vaccine has now been licensed in six countries. We propose that this live attenuated vaccine acts like a silent natural infection in priming or boosting host immunity. A transmission dynamic model incorporating this hypothesis fits recent clinical trial data well and predicts that vaccine effectiveness depends strongly on the age group vaccinated and local transmission intensity. Vaccination in low-transmission settings may increase the incidence of more severe &quot;secondary-like&quot; infection and, thus, the numbers hospitalized for dengue. In moderate transmission settings, we predict positive impacts overall but increased risks of hospitalization with dengue disease for individuals who are vaccinated when seronegative. However, in high-transmission settings, vaccination benefits both the whole population and seronegative recipients. Our analysis can help inform policy-makers evaluating this and other candidate dengue vaccines.</AbstractText>
                <CopyrightInformation>Copyright © 2016, American Association for the Advancement of Science.</CopyrightInformation>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Ferguson</LastName>
                    <ForeName>Neil M</ForeName>
                    <Initials>NM</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK. neil.ferguson@imperial.ac.uk.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Rodríguez-Barraquer</LastName>
                    <ForeName>Isabel</ForeName>
                    <Initials>I</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Dorigatti</LastName>
                    <ForeName>Ilaria</ForeName>
                    <Initials>I</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Mier-Y-Teran-Romero</LastName>
                    <ForeName>Luis</ForeName>
                    <Initials>L</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Laydon</LastName>
                    <ForeName>Daniel J</ForeName>
                    <Initials>DJ</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cummings</LastName>
                    <ForeName>Derek A T</ForeName>
                    <Initials>DA</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA. Department of Biology and Emerging Pathogens Institute, University of Florida, Post Office Box 100009, Gainesville, FL 32610, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>MR/K010174/1</GrantID>
                    <Agency>Medical Research Council</Agency>
                    <Country>United Kingdom</Country>
                </Grant>
                <Grant>
                    <GrantID>R01 AI114703</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
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                <PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType>
                <PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
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            <Country>United States</Country>
            <MedlineTA>Science</MedlineTA>
            <NlmUniqueID>0404511</NlmUniqueID>
            <ISSNLinking>0036-8075</ISSNLinking>
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            <Chemical>
                <RegistryNumber>0</RegistryNumber>
                <NameOfSubstance UI="D053059">Dengue Vaccines</NameOfSubstance>
            </Chemical>
            <Chemical>
                <RegistryNumber>0</RegistryNumber>
                <NameOfSubstance UI="D014613">Vaccines, Attenuated</NameOfSubstance>
            </Chemical>
        </ChemicalList>
        <CitationSubset>IM</CitationSubset>
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        <MeshHeadingList>
            <MeshHeading>
                <DescriptorName UI="D000293" MajorTopicYN="N">Adolescent</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D002648" MajorTopicYN="N">Child</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D002675" MajorTopicYN="N">Child, Preschool</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D002986" MajorTopicYN="N">Clinical Trials as Topic</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D003715" MajorTopicYN="N">Dengue</DescriptorName>
                <QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName>
                <QualifierName UI="Q000517" MajorTopicYN="Y">prevention &amp; control</QualifierName>
                <QualifierName UI="Q000635" MajorTopicYN="N">transmission</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D053059" MajorTopicYN="N">Dengue Vaccines</DescriptorName>
                <QualifierName UI="Q000008" MajorTopicYN="N">administration &amp; dosage</QualifierName>
                <QualifierName UI="Q000276" MajorTopicYN="Y">immunology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D003716" MajorTopicYN="Y">Dengue Virus</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D006760" MajorTopicYN="N">Hospitalization</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D000071497" MajorTopicYN="Y">Immunogenicity, Vaccine</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D008962" MajorTopicYN="N">Models, Theoretical</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D018570" MajorTopicYN="N">Risk Assessment</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D014611" MajorTopicYN="N">Vaccination</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D014613" MajorTopicYN="N">Vaccines, Attenuated</DescriptorName>
                <QualifierName UI="Q000008" MajorTopicYN="N">administration &amp; dosage</QualifierName>
                <QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName>
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                <Year>2016</Year>
                <Month>07</Month>
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            <Journal>
                <ISSN IssnType="Electronic">1091-6490</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>113</Volume>
                    <Issue>47</Issue>
                    <PubDate>
                        <Year>2016</Year>
                        <Month>Nov</Month>
                        <Day>22</Day>
                    </PubDate>
                </JournalIssue>
                <Title>Proceedings of the National Academy of Sciences of the United States of America</Title>
                <ISOAbbreviation>Proc. Natl. Acad. Sci. U.S.A.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>How social structures, space, and behaviors shape the spread of infectious diseases using chikungunya as a case study.</ArticleTitle>
            <Pagination>
                <MedlinePgn>13420-13425</MedlinePgn>
            </Pagination>
            <Abstract>
                <AbstractText>Whether an individual becomes infected in an infectious disease outbreak depends on many interconnected risk factors, which may relate to characteristics of the individual (e.g., age, sex), his or her close relatives (e.g., household members), or the wider community. Studies monitoring individuals in households or schools have helped elucidate the determinants of transmission in small social structures due to advances in statistical modeling; but such an approach has so far largely failed to consider individuals in the wider context they live in. Here, we used an outbreak of chikungunya in a rural community in Bangladesh as a case study to obtain a more comprehensive characterization of risk factors in disease spread. We developed Bayesian data augmentation approaches to account for uncertainty in the source of infection, recall uncertainty, and unobserved infection dates. We found that the probability of chikungunya transmission was 12% [95% credible interval (CI): 8-17%] between household members but dropped to 0.3% for those living 50 m away (95% CI: 0.2-0.5%). Overall, the mean transmission distance was 95 m (95% CI: 77-113 m). Females were 1.5 times more likely to become infected than males (95% CI: 1.2-1.8), which was virtually identical to the relative risk of being at home estimated from an independent human movement study in the country. Reported daily use of antimosquito coils had no detectable impact on transmission. This study shows how the complex interplay between the characteristics of an individual and his or her close and wider environment contributes to the shaping of infectious disease epidemics.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Salje</LastName>
                    <ForeName>Henrik</ForeName>
                    <Initials>H</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; hsalje@jhu.edu.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris 75015, France.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Centre National de la Recherche Scientifique, URA3012, Paris 75015, France.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Center of Bioinformatics, Biostatistics, and Integrative Biology, Institut Pasteur, Paris 75015, France.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Lessler</LastName>
                    <ForeName>Justin</ForeName>
                    <Initials>J</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Paul</LastName>
                    <ForeName>Kishor Kumar</ForeName>
                    <Initials>KK</Initials>
                    <Identifier Source="ORCID">http://orcid.org/0000-0002-6054-3571</Identifier>
                    <AffiliationInfo>
                        <Affiliation>Center for Communicable Diseases, International Centre for Diarrhoeal Disease Research, Bangladesh, Mohakhali, Dhaka 1212, Bangladesh.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Azman</LastName>
                    <ForeName>Andrew S</ForeName>
                    <Initials>AS</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Rahman</LastName>
                    <ForeName>M Waliur</ForeName>
                    <Initials>MW</Initials>
                    <AffiliationInfo>
                        <Affiliation>Center for Communicable Diseases, International Centre for Diarrhoeal Disease Research, Bangladesh, Mohakhali, Dhaka 1212, Bangladesh.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Institute of Epidemiology Disease Control &amp; Research, Mohakhali, Dhaka 1212, Bangladesh.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Rahman</LastName>
                    <ForeName>Mahmudur</ForeName>
                    <Initials>M</Initials>
                    <AffiliationInfo>
                        <Affiliation>Institute of Epidemiology Disease Control &amp; Research, Mohakhali, Dhaka 1212, Bangladesh.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cummings</LastName>
                    <ForeName>Derek</ForeName>
                    <Initials>D</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Department of Biology, University of Florida, Gainesville, FL 32603.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Gurley</LastName>
                    <ForeName>Emily S</ForeName>
                    <Initials>ES</Initials>
                    <AffiliationInfo>
                        <Affiliation>Center for Communicable Diseases, International Centre for Diarrhoeal Disease Research, Bangladesh, Mohakhali, Dhaka 1212, Bangladesh.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cauchemez</LastName>
                    <ForeName>Simon</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris 75015, France.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Centre National de la Recherche Scientifique, URA3012, Paris 75015, France.</Affiliation>
                    </AffiliationInfo>
                    <AffiliationInfo>
                        <Affiliation>Center of Bioinformatics, Biostatistics, and Integrative Biology, Institut Pasteur, Paris 75015, France.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>R01 AI102939</GrantID>
                    <Acronym>AI</Acronym>
                    <Agency>NIAID NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U01 CI000628</GrantID>
                    <Acronym>CI</Acronym>
                    <Agency>NCPDCID CDC HHS</Agency>
                    <Country>United States</Country>
                </Grant>
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>07</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>Proc Natl Acad Sci U S A</MedlineTA>
            <NlmUniqueID>7505876</NlmUniqueID>
            <ISSNLinking>0027-8424</ISSNLinking>
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        </CommentsCorrectionsList>
        <KeywordList Owner="NOTNLM">
            <Keyword MajorTopicYN="N">Bayesian</Keyword>
            <Keyword MajorTopicYN="N">chikungunya</Keyword>
            <Keyword MajorTopicYN="N">data augmentation</Keyword>
            <Keyword MajorTopicYN="N">outbreaks</Keyword>
            <Keyword MajorTopicYN="N">spatial spread</Keyword>
        </KeywordList>
        <CoiStatement>The authors declare no conflict of interest.</CoiStatement>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>9</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="medline">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>9</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="entrez">
                <Year>2016</Year>
                <Month>11</Month>
                <Day>9</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
        </History>
        <PublicationStatus>ppublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">27821727</ArticleId>
            <ArticleId IdType="pii">1611391113</ArticleId>
            <ArticleId IdType="doi">10.1073/pnas.1611391113</ArticleId>
            <ArticleId IdType="pmc">PMC5127331</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">28007555</PMID>
        <DateCreated>
            <Year>2016</Year>
            <Month>12</Month>
            <Day>23</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>08</Day>
        </DateRevised>
        <Article PubModel="Print-Electronic">
            <Journal>
                <ISSN IssnType="Electronic">1095-8541</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>415</Volume>
                    <PubDate>
                        <Year>2017</Year>
                        <Month>Feb</Month>
                        <Day>21</Day>
                    </PubDate>
                </JournalIssue>
                <Title>Journal of theoretical biology</Title>
                <ISOAbbreviation>J. Theor. Biol.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Regular biennial cycles in epidemics caused by parametric resonance.</ArticleTitle>
            <Pagination>
                <MedlinePgn>137-144</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="pii" ValidYN="Y">S0022-5193(16)30424-6</ELocationID>
            <ELocationID EIdType="doi" ValidYN="Y">10.1016/j.jtbi.2016.12.013</ELocationID>
            <Abstract>
                <AbstractText>The interaction between nonlinearity and seasonal forcing in childhood infectious diseases often leads to multiyear cycles with large amplitude. Regular biennial cycles in particular were observed in measles reports throughout the world. The objective of this paper is to understand the mechanism of such biennial cycles, especially the conditions under which the large amplitude biennial oscillation might appear. It is proposed that such biennial cycles are caused by parametric resonance, which might occur when varying the parameter at a frequency close to twice the natural frequency of the system. The analysis is carried out by solving an SIR model semi-analytically using method of multiple scales (MMS). This analysis shows how parametric resonance occurs due to the interaction between nonlinearity and periodic forcing. Using the MMS solution, the boundary between the resonance region and the non-resonance region in the parameter space is obtained. The effects of different parameters on the triggering of parametric resonance are studied, such as transmission rate, recovery rate, birth rate and amplitude of seasonality. The effects of stochasticity on the onset of parametric resonance are also studied.</AbstractText>
                <CopyrightInformation>Copyright © 2016 Elsevier Ltd. All rights reserved.</CopyrightInformation>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Chen</LastName>
                    <ForeName>Shiyang</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Mechanical Engineering, University of Michigan, 2350 Hayward Street, Ann Arbor, MI 48109, USA. Electronic address: shychen@umich.edu.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Epureanu</LastName>
                    <ForeName>Bogdan</ForeName>
                    <Initials>B</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Mechanical Engineering, University of Michigan, 2350 Hayward Street, Ann Arbor, MI 48109, USA. Electronic address: epureanu@umich.edu.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>U01 GM110744</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>20</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>England</Country>
            <MedlineTA>J Theor Biol</MedlineTA>
            <NlmUniqueID>0376342</NlmUniqueID>
            <ISSNLinking>0022-5193</ISSNLinking>
        </MedlineJournalInfo>
        <KeywordList Owner="NOTNLM">
            <Keyword MajorTopicYN="N">Childhood infectious diseases</Keyword>
            <Keyword MajorTopicYN="N">Method of multiple scales</Keyword>
            <Keyword MajorTopicYN="N">SIR model</Keyword>
        </KeywordList>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="received">
                <Year>2016</Year>
                <Month>03</Month>
                <Day>10</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="revised">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>13</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>17</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>23</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="medline">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>23</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="entrez">
                <Year>2016</Year>
                <Month>12</Month>
                <Day>24</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
        </History>
        <PublicationStatus>ppublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28007555</ArticleId>
            <ArticleId IdType="pii">S0022-5193(16)30424-6</ArticleId>
            <ArticleId IdType="doi">10.1016/j.jtbi.2016.12.013</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="Publisher" Owner="NLM">
        <PMID Version="1">28200111</PMID>
        <DateCreated>
            <Year>2017</Year>
            <Month>02</Month>
            <Day>15</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>03</Day>
        </DateRevised>
        <Article PubModel="Print-Electronic">
            <Journal>
                <ISSN IssnType="Electronic">1476-6256</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <PubDate>
                        <Year>2017</Year>
                        <Month>Feb</Month>
                        <Day>15</Day>
                    </PubDate>
                </JournalIssue>
                <Title>American journal of epidemiology</Title>
                <ISOAbbreviation>Am. J. Epidemiol.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Real-Time Assessment of Health-Care Requirements During the Zika Virus Epidemic in Martinique.</ArticleTitle>
            <Pagination>
                <MedlinePgn>1-10</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.1093/aje/kwx008</ELocationID>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Andronico</LastName>
                    <ForeName>Alessio</ForeName>
                    <Initials>A</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Dorléans</LastName>
                    <ForeName>Frédérique</ForeName>
                    <Initials>F</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Fergé</LastName>
                    <ForeName>Jean-Louis</ForeName>
                    <Initials>JL</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Salje</LastName>
                    <ForeName>Henrik</ForeName>
                    <Initials>H</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ghawché</LastName>
                    <ForeName>Frédéric</ForeName>
                    <Initials>F</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Signate</LastName>
                    <ForeName>Aissatou</ForeName>
                    <Initials>A</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Daudens-Vaysse</LastName>
                    <ForeName>Elise</ForeName>
                    <Initials>E</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Baudouin</LastName>
                    <ForeName>Laure</ForeName>
                    <Initials>L</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Dub</LastName>
                    <ForeName>Timothée</ForeName>
                    <Initials>T</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Aubry</LastName>
                    <ForeName>Maite</ForeName>
                    <Initials>M</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cao-Lormeau</LastName>
                    <ForeName>Van-Mai</ForeName>
                    <Initials>VM</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Ledrans</LastName>
                    <ForeName>Martine</ForeName>
                    <Initials>M</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Noel</LastName>
                    <ForeName>Harold</ForeName>
                    <Initials>H</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Mallet</LastName>
                    <ForeName>Henri-Pierre</ForeName>
                    <Initials>HP</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Fontanet</LastName>
                    <ForeName>Arnaud</ForeName>
                    <Initials>A</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cabié</LastName>
                    <ForeName>André</ForeName>
                    <Initials>A</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cauchemez</LastName>
                    <ForeName>Simon</ForeName>
                    <Initials>S</Initials>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>U01 GM110721</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
            <ArticleDate DateType="Electronic">
                <Year>2017</Year>
                <Month>02</Month>
                <Day>15</Day>
            </ArticleDate>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>Am J Epidemiol</MedlineTA>
            <NlmUniqueID>7910653</NlmUniqueID>
            <ISSNLinking>0002-9262</ISSNLinking>
        </MedlineJournalInfo>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="received">
                <Year>2016</Year>
                <Month>10</Month>
                <Day>07</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="accepted">
                <Year>2017</Year>
                <Month>01</Month>
                <Day>05</Day>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="entrez">
                <Year>2017</Year>
                <Month>2</Month>
                <Day>16</Day>
                <Hour>6</Hour>
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                <Hour>6</Hour>
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                <Year>2017</Year>
                <Month>2</Month>
                <Day>16</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
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        </History>
        <PublicationStatus>aheadofprint</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28200111</ArticleId>
            <ArticleId IdType="pii">2996591</ArticleId>
            <ArticleId IdType="doi">10.1093/aje/kwx008</ArticleId>
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<PubmedArticle>
    <MedlineCitation Status="In-Process" Owner="NLM">
        <PMID Version="1">28221131</PMID>
        <DateCreated>
            <Year>2017</Year>
            <Month>02</Month>
            <Day>21</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>04</Month>
            <Day>08</Day>
        </DateRevised>
        <Article PubModel="Print">
            <Journal>
                <ISSN IssnType="Electronic">1080-6059</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>23</Volume>
                    <Issue>3</Issue>
                    <PubDate>
                        <Year>2017</Year>
                        <Month>Mar</Month>
                    </PubDate>
                </JournalIssue>
                <Title>Emerging infectious diseases</Title>
                <ISOAbbreviation>Emerging Infect. Dis.</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Spatiotemporal Fluctuations and Triggers of Ebola Virus Spillover.</ArticleTitle>
            <Pagination>
                <MedlinePgn>415-422</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.3201/eid2303.160101</ELocationID>
            <Abstract>
                <AbstractText>Because the natural reservoir of Ebola virus remains unclear and disease outbreaks in humans have occurred only sporadically over a large region, forecasting when and where Ebola spillovers are most likely to occur constitutes a continuing and urgent public health challenge. We developed a statistical modeling approach that associates 37 human or great ape Ebola spillovers since 1982 with spatiotemporally dynamic covariates including vegetative cover, human population size, and absolute and relative rainfall over 3 decades across sub-Saharan Africa. Our model (area under the curve 0.80 on test data) shows that spillover intensity is highest during transitions between wet and dry seasons; overall, high seasonal intensity occurs over much of tropical Africa; and spillover intensity is greatest at high (&gt;1,000/km(2)) and very low (&lt;100/km(2)) human population densities compared with intermediate levels. These results suggest strong seasonality in Ebola spillover from wild reservoirs and indicate particular times and regions for targeted surveillance.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Schmidt</LastName>
                    <ForeName>John Paul</ForeName>
                    <Initials>JP</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Park</LastName>
                    <ForeName>Andrew W</ForeName>
                    <Initials>AW</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kramer</LastName>
                    <ForeName>Andrew M</ForeName>
                    <Initials>AM</Initials>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Han</LastName>
                    <ForeName>Barbara A</ForeName>
                    <Initials>BA</Initials>
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                <Author ValidYN="Y">
                    <LastName>Alexander</LastName>
                    <ForeName>Laura W</ForeName>
                    <Initials>LW</Initials>
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                <Author ValidYN="Y">
                    <LastName>Drake</LastName>
                    <ForeName>John M</ForeName>
                    <Initials>JM</Initials>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <GrantList CompleteYN="Y">
                <Grant>
                    <GrantID>U01 GM110744</GrantID>
                    <Acronym>GM</Acronym>
                    <Agency>NIGMS NIH HHS</Agency>
                    <Country>United States</Country>
                </Grant>
            </GrantList>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
        </Article>
        <MedlineJournalInfo>
            <Country>United States</Country>
            <MedlineTA>Emerg Infect Dis</MedlineTA>
            <NlmUniqueID>9508155</NlmUniqueID>
            <ISSNLinking>1080-6040</ISSNLinking>
        </MedlineJournalInfo>
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        <KeywordList Owner="NOTNLM">
            <Keyword MajorTopicYN="N">Ebola virus</Keyword>
            <Keyword MajorTopicYN="N">disease outbreaks</Keyword>
            <Keyword MajorTopicYN="N">early warning system</Keyword>
            <Keyword MajorTopicYN="N">infectious diseases</Keyword>
            <Keyword MajorTopicYN="N">modeling</Keyword>
            <Keyword MajorTopicYN="N">seasonality</Keyword>
            <Keyword MajorTopicYN="N">spatiotemporal forecasting</Keyword>
            <Keyword MajorTopicYN="N">spillovers</Keyword>
            <Keyword MajorTopicYN="N">viruses</Keyword>
        </KeywordList>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="entrez">
                <Year>2017</Year>
                <Month>2</Month>
                <Day>22</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2017</Year>
                <Month>2</Month>
                <Day>22</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="medline">
                <Year>2017</Year>
                <Month>2</Month>
                <Day>22</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
        </History>
        <PublicationStatus>ppublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28221131</ArticleId>
            <ArticleId IdType="doi">10.3201/eid2303.160101</ArticleId>
            <ArticleId IdType="pmc">PMC5382727</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>


<PubmedArticle>
    <MedlineCitation Status="Publisher" Owner="NLM">
        <PMID Version="1">25083415</PMID>
        <DateCreated>
            <Year>2014</Year>
            <Month>08</Month>
            <Day>01</Day>
        </DateCreated>
        <DateRevised>
            <Year>2017</Year>
            <Month>03</Month>
            <Day>22</Day>
        </DateRevised>
        <Article PubModel="Print">
            <Journal>
                <ISSN IssnType="Electronic">2214-109X</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>2</Volume>
                    <Issue>1</Issue>
                    <PubDate>
                        <Year>2013</Year>
                        <Month>Dec</Month>
                        <Day>10</Day>
                    </PubDate>
                </JournalIssue>
                <Title>The Lancet. Global health</Title>
                <ISOAbbreviation>Lancet Glob Health</ISOAbbreviation>
            </Journal>
            <ArticleTitle>Health benefits, costs, and cost-effectiveness of earlier eligibility for adult antiretroviral therapy and expanded treatment coverage: a combined analysis of 12 mathematical models.</ArticleTitle>
            <Pagination>
                <MedlinePgn>23-34</MedlinePgn>
            </Pagination>
            <Abstract>
                <AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">New WHO guidelines recommend ART initiation for HIV-positive persons with CD4 cell counts ≤500 cells/µL, a higher threshold than was previously recommended. Country decision makers must consider whether to further expand ART eligibility accordingly.</AbstractText>
                <AbstractText Label="METHODS" NlmCategory="METHODS">We used multiple independent mathematical models in four settings-South Africa, Zambia, India, and Vietnam-to evaluate the potential health impact, costs, and cost-effectiveness of different adult ART eligibility criteria under scenarios of current and expanded treatment coverage, with results projected over 20 years. Analyses considered extending eligibility to include individuals with CD4 ≤500 cells/µL or all HIV-positive adults, compared to the previous recommendation of initiation with CD4 ≤350 cells/µL. We assessed costs from a health system perspective, and calculated the incremental cost per DALY averted ($/DALY) to compare competing strategies. Strategies were considered 'very cost-effective' if the $/DALY was less than the country's per capita gross domestic product (GDP; South Africa: $8040, Zambia: $1425, India: $1489, Vietnam: $1407) and 'cost-effective' if $/DALY was less than three times per capita GDP.</AbstractText>
                <AbstractText Label="FINDINGS" NlmCategory="RESULTS">In South Africa, the cost per DALY averted of extending ART eligibility to CD4 ≤500 cells/µL ranged from $237 to $1691/DALY compared to 2010 guidelines; in Zambia, expanded eligibility ranged from improving health outcomes while reducing costs (i.e. dominating current guidelines) to $749/DALY. Results were similar in scenarios with substantially expanded treatment access and for expanding eligibility to all HIV-positive adults. Expanding treatment coverage in the general population was therefore found to be cost-effective. In India, eligibility for all HIV-positive persons ranged from $131 to $241/DALY and in Vietnam eligibility for CD4 ≤500 cells/µL cost $290/DALY. In concentrated epidemics, expanded access among key populations was also cost-effective.</AbstractText>
                <AbstractText Label="INTERPRETATION" NlmCategory="CONCLUSIONS">Earlier ART eligibility is estimated to be very cost-effective in low- and middle-income settings, although these questions should be revisited as further information becomes available. Scaling-up ART should be considered among other high-priority health interventions competing for health budgets.</AbstractText>
                <AbstractText Label="FUNDING" NlmCategory="BACKGROUND">The Bill and Melinda Gates Foundation and World Health Organization.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Eaton</LastName>
                    <ForeName>Jeffrey W</ForeName>
                    <Initials>JW</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, Imperial College London, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Menzies</LastName>
                    <ForeName>Nicolas A</ForeName>
                    <Initials>NA</Initials>
                    <AffiliationInfo>
                        <Affiliation>Center for Health Decision Science, Harvard School of Public Health, Boston, MA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Stover</LastName>
                    <ForeName>John</ForeName>
                    <Initials>J</Initials>
                    <AffiliationInfo>
                        <Affiliation>Futures Institute, Glastonbury, CT, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cambiano</LastName>
                    <ForeName>Valentina</ForeName>
                    <Initials>V</Initials>
                    <AffiliationInfo>
                        <Affiliation>Research Department of Infection and Population Health, University College London, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Chindelevitch</LastName>
                    <ForeName>Leonid</ForeName>
                    <Initials>L</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Global Health and Population, Harvard School of Public Health, Boston, MA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Cori</LastName>
                    <ForeName>Anne</ForeName>
                    <Initials>A</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Hontelez</LastName>
                    <ForeName>Jan A C</ForeName>
                    <Initials>JA</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands ; Africa Centre for Health and Population Studies, University of KwaZulu-Natal, Mtubatuba, South Africa ; Nijmegen International Center for Health System Analysis and Education (NICHE), Department of Primary and Community Care, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Humair</LastName>
                    <ForeName>Salal</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Harvard School of Public Health, Boston, MA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Kerr</LastName>
                    <ForeName>Cliff C</ForeName>
                    <Initials>CC</Initials>
                    <AffiliationInfo>
                        <Affiliation>Kirby Institute, University of New South Wales, Sydney, Australia.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Klein</LastName>
                    <ForeName>Daniel J</ForeName>
                    <Initials>DJ</Initials>
                    <AffiliationInfo>
                        <Affiliation>Epidemiological Modeling Group, Intellectual Ventures Laboratory, Bellevue, WA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Mishra</LastName>
                    <ForeName>Sharmistha</ForeName>
                    <Initials>S</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, Imperial College London, London, UK ; Division of Infectious Diseases, St. Michael's Hospital, University of Toronto, Canada.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Mitchell</LastName>
                    <ForeName>Kate M</ForeName>
                    <Initials>KM</Initials>
                    <AffiliationInfo>
                        <Affiliation>Social and Mathematical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Nichols</LastName>
                    <ForeName>Brooke E</ForeName>
                    <Initials>BE</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Virology, Erasmus Medical Center, Rotterdam, Netherlands.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Vickerman</LastName>
                    <ForeName>Peter</ForeName>
                    <Initials>P</Initials>
                    <AffiliationInfo>
                        <Affiliation>Social and Mathematical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Bakker</LastName>
                    <ForeName>Roel</ForeName>
                    <Initials>R</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Bärnighausen</LastName>
                    <ForeName>Till</ForeName>
                    <Initials>T</Initials>
                    <AffiliationInfo>
                        <Affiliation>Africa Centre for Health and Population Studies, University of KwaZulu-Natal, Mtubatuba, South Africa ; Harvard School of Public Health, Boston, MA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Bershteyn</LastName>
                    <ForeName>Anna</ForeName>
                    <Initials>A</Initials>
                    <AffiliationInfo>
                        <Affiliation>Epidemiological Modeling Group, Intellectual Ventures Laboratory, Bellevue, WA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Bloom</LastName>
                    <ForeName>David E</ForeName>
                    <Initials>DE</Initials>
                    <AffiliationInfo>
                        <Affiliation>Harvard School of Public Health, Boston, MA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Boily</LastName>
                    <ForeName>Marie-Claude</ForeName>
                    <Initials>MC</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, Imperial College London, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Chang</LastName>
                    <ForeName>Stewart T</ForeName>
                    <Initials>ST</Initials>
                    <AffiliationInfo>
                        <Affiliation>Epidemiological Modeling Group, Intellectual Ventures Laboratory, Bellevue, WA, USA.</Affiliation>
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                <Author ValidYN="Y">
                    <LastName>Cohen</LastName>
                    <ForeName>Ted</ForeName>
                    <Initials>T</Initials>
                    <AffiliationInfo>
                        <Affiliation>Division of Global Health Equity, Brigham and Women's Hospital, Boston, MA, USA ; Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Dodd</LastName>
                    <ForeName>Peter J</ForeName>
                    <Initials>PJ</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, London School of Hygiene &amp; Tropical Medicine, London, UK.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Fraser</LastName>
                    <ForeName>Christophe</ForeName>
                    <Initials>C</Initials>
                    <AffiliationInfo>
                        <Affiliation>MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.</Affiliation>
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                </Author>
                <Author ValidYN="Y">
                    <LastName>Gopalappa</LastName>
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                    <Initials>C</Initials>
                    <AffiliationInfo>
                        <Affiliation>Futures Institute, Glastonbury, CT, USA.</Affiliation>
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                    <LastName>Lundgren</LastName>
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                    <AffiliationInfo>
                        <Affiliation>Copenhagen University Hospital/Rigshospitalet, Copenhagen, Denmark ; University of Copenhagen, Copenhagen, Denmark.</Affiliation>
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                <Author ValidYN="Y">
                    <LastName>Martin</LastName>
                    <ForeName>Natasha K</ForeName>
                    <Initials>NK</Initials>
                    <AffiliationInfo>
                        <Affiliation>School of Social and Community Medicine, University of Bristol, Bristol, UK ; Social and Mathematical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK.</Affiliation>
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                </Author>
                <Author ValidYN="Y">
                    <LastName>Mikkelsen</LastName>
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                    <Initials>E</Initials>
                    <AffiliationInfo>
                        <Affiliation>Nijmegen International Center for Health System Analysis and Education (NICHE), Department of Primary and Community Care, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands.</Affiliation>
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                </Author>
                <Author ValidYN="Y">
                    <LastName>Mountain</LastName>
                    <ForeName>Elisa</ForeName>
                    <Initials>E</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Infectious Disease Epidemiology, Imperial College London, London, UK.</Affiliation>
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                <Author ValidYN="Y">
                    <LastName>Pham</LastName>
                    <ForeName>Quang D</ForeName>
                    <Initials>QD</Initials>
                    <AffiliationInfo>
                        <Affiliation>Kirby Institute, University of New South Wales, Sydney, Australia.</Affiliation>
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                    <LastName>Pickles</LastName>
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                        <Affiliation>Department of Infectious Disease Epidemiology, Imperial College London, London, UK.</Affiliation>
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                        <Affiliation>Research Department of Infection and Population Health, University College London, London, UK.</Affiliation>
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                        <Affiliation>Social and Mathematical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK.</Affiliation>
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                        <Affiliation>Futures Institute, Glastonbury, CT, USA.</Affiliation>
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