Comparing nonpharmaceutical interventions for containing emerging epidemics
Identifieur interne : 000D18 ( Pmc/Corpus ); précédent : 000D17; suivant : 000D19Comparing nonpharmaceutical interventions for containing emerging epidemics
Auteurs : Corey M. Peak ; Lauren M. Childs ; Yonatan H. Grad ; Caroline O. BuckeeSource :
- Proceedings of the National Academy of Sciences of the United States of America [ 0027-8424 ] ; 2017.
Abstract
Quarantine and symptom monitoring of contacts with suspected exposure to an infectious disease are key interventions for the control of emerging epidemics; however, there does not yet exist a quantitative framework for comparing the control performance of each intervention. Here, we use a mathematical model of seven case-study diseases to show how the choice of intervention is influenced by the natural history of the infectious disease, its inherent transmissibility, and the intervention feasibility in the particular healthcare setting. We use this information to identify the most important characteristics of the disease and setting that need to be considered for an emerging pathogen to make an informed decision between quarantine and symptom monitoring.
Url:
DOI: 10.1073/pnas.1616438114
PubMed: 28351976
PubMed Central: 5393248
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Comparing nonpharmaceutical interventions for containing emerging epidemics</title><author><name sortKey="Peak, Corey M" sort="Peak, Corey M" uniqKey="Peak C" first="Corey M." last="Peak">Corey M. Peak</name><affiliation><nlm:aff id="aff1">Center for Communicable Disease Dynamics, Department of Epidemiology,<institution>Harvard T.H. Chan School of Public Health</institution>, Boston,<addr-line>MA</addr-line> 02115;</nlm:aff></affiliation></author><author><name sortKey="Childs, Lauren M" sort="Childs, Lauren M" uniqKey="Childs L" first="Lauren M." last="Childs">Lauren M. Childs</name><affiliation><nlm:aff id="aff1">Center for Communicable Disease Dynamics, Department of Epidemiology,<institution>Harvard T.H. Chan School of Public Health</institution>, Boston,<addr-line>MA</addr-line> 02115;</nlm:aff></affiliation></author><author><name sortKey="Grad, Yonatan H" sort="Grad, Yonatan H" uniqKey="Grad Y" first="Yonatan H." last="Grad">Yonatan H. Grad</name><affiliation><nlm:aff id="aff2">Department of Immunology and Infectious Diseases,<institution>Harvard T.H. Chan School of Public Health</institution>, Boston,<addr-line>MA</addr-line> 02115;</nlm:aff></affiliation><affiliation><nlm:aff id="aff3">Division of Infectious Diseases, Brigham and Women’s Hospital,<institution>Harvard Medical School</institution>, Boston,<addr-line>MA</addr-line> 02115</nlm:aff></affiliation></author><author><name sortKey="Buckee, Caroline O" sort="Buckee, Caroline O" uniqKey="Buckee C" first="Caroline O." last="Buckee">Caroline O. Buckee</name><affiliation><nlm:aff id="aff1">Center for Communicable Disease Dynamics, Department of Epidemiology,<institution>Harvard T.H. Chan School of Public Health</institution>, Boston,<addr-line>MA</addr-line> 02115;</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">28351976</idno><idno type="pmc">5393248</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5393248</idno><idno type="RBID">PMC:5393248</idno><idno type="doi">10.1073/pnas.1616438114</idno><date when="2017">2017</date><idno type="wicri:Area/Pmc/Corpus">000D18</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000D18</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Comparing nonpharmaceutical interventions for containing emerging epidemics</title><author><name sortKey="Peak, Corey M" sort="Peak, Corey M" uniqKey="Peak C" first="Corey M." last="Peak">Corey M. Peak</name><affiliation><nlm:aff id="aff1">Center for Communicable Disease Dynamics, Department of Epidemiology,<institution>Harvard T.H. Chan School of Public Health</institution>, Boston,<addr-line>MA</addr-line> 02115;</nlm:aff></affiliation></author><author><name sortKey="Childs, Lauren M" sort="Childs, Lauren M" uniqKey="Childs L" first="Lauren M." last="Childs">Lauren M. Childs</name><affiliation><nlm:aff id="aff1">Center for Communicable Disease Dynamics, Department of Epidemiology,<institution>Harvard T.H. Chan School of Public Health</institution>, Boston,<addr-line>MA</addr-line> 02115;</nlm:aff></affiliation></author><author><name sortKey="Grad, Yonatan H" sort="Grad, Yonatan H" uniqKey="Grad Y" first="Yonatan H." last="Grad">Yonatan H. Grad</name><affiliation><nlm:aff id="aff2">Department of Immunology and Infectious Diseases,<institution>Harvard T.H. Chan School of Public Health</institution>, Boston,<addr-line>MA</addr-line> 02115;</nlm:aff></affiliation><affiliation><nlm:aff id="aff3">Division of Infectious Diseases, Brigham and Women’s Hospital,<institution>Harvard Medical School</institution>, Boston,<addr-line>MA</addr-line> 02115</nlm:aff></affiliation></author><author><name sortKey="Buckee, Caroline O" sort="Buckee, Caroline O" uniqKey="Buckee C" first="Caroline O." last="Buckee">Caroline O. Buckee</name><affiliation><nlm:aff id="aff1">Center for Communicable Disease Dynamics, Department of Epidemiology,<institution>Harvard T.H. Chan School of Public Health</institution>, Boston,<addr-line>MA</addr-line> 02115;</nlm:aff></affiliation></author></analytic><series><title level="j">Proceedings of the National Academy of Sciences of the United States of America</title><idno type="ISSN">0027-8424</idno><idno type="eISSN">1091-6490</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Significance</title><p>Quarantine and symptom monitoring of contacts with suspected exposure to an infectious disease are key interventions for the control of emerging epidemics; however, there does not yet exist a quantitative framework for comparing the control performance of each intervention. Here, we use a mathematical model of seven case-study diseases to show how the choice of intervention is influenced by the natural history of the infectious disease, its inherent transmissibility, and the intervention feasibility in the particular healthcare setting. We use this information to identify the most important characteristics of the disease and setting that need to be considered for an emerging pathogen to make an informed decision between quarantine and symptom monitoring.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Proc Natl Acad Sci U S A</journal-id><journal-id journal-id-type="iso-abbrev">Proc. Natl. Acad. Sci. U.S.A</journal-id><journal-id journal-id-type="hwp">pnas</journal-id><journal-id journal-id-type="pmc">pnas</journal-id><journal-id journal-id-type="publisher-id">PNAS</journal-id><journal-title-group><journal-title>Proceedings of the National Academy of Sciences of the United States of America</journal-title></journal-title-group><issn pub-type="ppub">0027-8424</issn><issn pub-type="epub">1091-6490</issn><publisher><publisher-name>National Academy of Sciences</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">28351976</article-id><article-id pub-id-type="pmc">5393248</article-id><article-id pub-id-type="publisher-id">201616438</article-id><article-id pub-id-type="doi">10.1073/pnas.1616438114</article-id><article-categories><subj-group subj-group-type="heading"><subject>Biological Sciences</subject><subj-group><subject>Population Biology</subject></subj-group></subj-group></article-categories><title-group><article-title>Comparing nonpharmaceutical interventions for containing emerging epidemics</article-title><alt-title alt-title-type="short">Pathogen dynamics determine containment strategies</alt-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="false">http://orcid.org/0000-0001-6150-5318</contrib-id><name><surname>Peak</surname><given-names>Corey M.</given-names></name><xref ref-type="aff" rid="aff1"><sup>a</sup></xref></contrib><contrib contrib-type="author"><name><surname>Childs</surname><given-names>Lauren M.</given-names></name><xref ref-type="aff" rid="aff1"><sup>a</sup></xref><xref ref-type="author-notes" rid="fn1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="false">http://orcid.org/0000-0001-5646-1314</contrib-id><name><surname>Grad</surname><given-names>Yonatan H.</given-names></name><xref ref-type="aff" rid="aff2"><sup>b</sup></xref><xref ref-type="aff" rid="aff3"><sup>c</sup></xref></contrib><contrib contrib-type="author"><name><surname>Buckee</surname><given-names>Caroline O.</given-names></name><xref ref-type="aff" rid="aff1"><sup>a</sup></xref><xref ref-type="corresp" rid="cor1"><sup>2</sup></xref></contrib><aff id="aff1"><sup>a</sup>Center for Communicable Disease Dynamics, Department of Epidemiology,<institution>Harvard T.H. Chan School of Public Health</institution>, Boston,<addr-line>MA</addr-line> 02115;</aff><aff id="aff2"><sup>b</sup>Department of Immunology and Infectious Diseases,<institution>Harvard T.H. Chan School of Public Health</institution>, Boston,<addr-line>MA</addr-line> 02115;</aff><aff id="aff3"><sup>c</sup>Division of Infectious Diseases, Brigham and Women’s Hospital,<institution>Harvard Medical School</institution>, Boston,<addr-line>MA</addr-line> 02115</aff></contrib-group><author-notes><corresp id="cor1"><sup>2</sup>To whom correspondence should be addressed. Email: <email>cbuckee@hsph.harvard.edu</email>.</corresp><fn fn-type="edited-by"><p>Edited by Simon A. Levin, Princeton University, Princeton, NJ, and approved February 22, 2017 (received for review October 3, 2016)</p></fn><fn fn-type="con"><p>Author contributions: C.M.P., L.M.C., Y.H.G., and C.O.B. designed research; C.M.P. performed research; C.M.P. and C.O.B. analyzed data; and C.M.P., L.M.C., Y.H.G., and C.O.B. wrote the paper.</p></fn><fn fn-type="present-address" id="fn1"><p><sup>1</sup>Present address: Department of Mathematics, Virginia Tech, Blacksburg, VA 24060.</p></fn><fn fn-type="COI-statement"><p>The authors declare no conflict of interest.</p></fn></author-notes><pub-date pub-type="ppub"><day>11</day><month>4</month><year>2017</year></pub-date><pub-date pub-type="epub"><day>28</day><month>3</month><year>2017</year></pub-date><pub-date pub-type="pmc-release"><day>28</day><month>3</month><year>2017</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
<volume>114</volume><issue>15</issue><fpage>4023</fpage><lpage>4028</lpage><permissions><license license-type="open-access"><license-p>Freely available online through the PNAS open access option.</license-p></license></permissions><self-uri xlink:title="pdf" xlink:href="pnas.201616438.pdf"></self-uri><abstract abstract-type="executive-summary"><title>Significance</title><p>Quarantine and symptom monitoring of contacts with suspected exposure to an infectious disease are key interventions for the control of emerging epidemics; however, there does not yet exist a quantitative framework for comparing the control performance of each intervention. Here, we use a mathematical model of seven case-study diseases to show how the choice of intervention is influenced by the natural history of the infectious disease, its inherent transmissibility, and the intervention feasibility in the particular healthcare setting. We use this information to identify the most important characteristics of the disease and setting that need to be considered for an emerging pathogen to make an informed decision between quarantine and symptom monitoring.</p></abstract><abstract><p>Strategies for containing an emerging infectious disease outbreak must be nonpharmaceutical when drugs or vaccines for the pathogen do not yet exist or are unavailable. The success of these nonpharmaceutical strategies will depend on not only the effectiveness of isolation measures but also the epidemiological characteristics of the infection. However, there is currently no systematic framework to assess the relationship between different containment strategies and the natural history and epidemiological dynamics of the pathogen. Here, we compare the effectiveness of quarantine and symptom monitoring, implemented via contact tracing, in controlling epidemics using an agent-based branching model. We examine the relationship between epidemic containment and the disease dynamics of symptoms and infectiousness for seven case-study diseases with diverse natural histories, including Ebola, influenza A, and severe acute respiratory syndrome (SARS). We show that the comparative effectiveness of symptom monitoring and quarantine depends critically on the natural history of the infectious disease, its inherent transmissibility, and the intervention feasibility in the particular healthcare setting. The benefit of quarantine over symptom monitoring is generally maximized for fast-course diseases, but we show the conditions under which symptom monitoring alone can control certain outbreaks. This quantitative framework can guide policymakers on how best to use nonpharmaceutical interventions and prioritize research during an outbreak of an emerging pathogen.</p></abstract><kwd-group><kwd>epidemiology</kwd><kwd>quarantine</kwd><kwd>active symptom monitoring</kwd><kwd>contact tracing</kwd><kwd>infectious disease dynamics</kwd></kwd-group><funding-group><award-group id="gs1"><funding-source id="sp1">HHS | NIH | National Institute of General Medical Sciences (NIGMS)<named-content content-type="funder-id">100000057</named-content></funding-source><award-id rid="sp1">U54GM088558</award-id></award-group><award-group id="gs2"><funding-source id="sp2">HHS | National Institutes of Health (NIH)<named-content content-type="funder-id">100000002</named-content></funding-source><award-id rid="sp2">T32AI007535-16A1</award-id></award-group><award-group id="gs3"><funding-source id="sp3">HHS | National Institutes of Health (NIH)<named-content content-type="funder-id">100000002</named-content></funding-source><award-id rid="sp3">K08-AI104767</award-id></award-group></funding-group><counts><page-count count="6"></page-count></counts></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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