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Modeling targeted layered containment of an influenza pandemic in the United States

Identifieur interne : 000768 ( Pmc/Curation ); précédent : 000767; suivant : 000769

Modeling targeted layered containment of an influenza pandemic in the United States

Auteurs : M. Elizabeth Halloran [États-Unis] ; Neil M. Ferguson [Royaume-Uni] ; Stephen Eubank [États-Unis] ; Ira M. Longini [États-Unis] ; Derek A. T. Cummings [Royaume-Uni] ; Bryan Lewis [États-Unis] ; Shufu Xu [États-Unis] ; Christophe Fraser [Royaume-Uni] ; Anil Vullikanti [États-Unis] ; Timothy C. Germann [États-Unis] ; Diane Wagener [États-Unis] ; Richard Beckman [États-Unis] ; Kai Kadau [États-Unis] ; Chris Barrett [États-Unis] ; Catherine A. Macken [États-Unis] ; Donald S. Burke [États-Unis] ; Philip Cooley [États-Unis]

Source :

RBID : PMC:2290797

Abstract

Planning a response to an outbreak of a pandemic strain of influenza is a high public health priority. Three research groups using different individual-based, stochastic simulation models have examined the consequences of intervention strategies chosen in consultation with U.S. public health workers. The first goal is to simulate the effectiveness of a set of potentially feasible intervention strategies. Combinations called targeted layered containment (TLC) of influenza antiviral treatment and prophylaxis and nonpharmaceutical interventions of quarantine, isolation, school closure, community social distancing, and workplace social distancing are considered. The second goal is to examine the robustness of the results to model assumptions. The comparisons focus on a pandemic outbreak in a population similar to that of Chicago, with ≈8.6 million people. The simulations suggest that at the expected transmissibility of a pandemic strain, timely implementation of a combination of targeted household antiviral prophylaxis, and social distancing measures could substantially lower the illness attack rate before a highly efficacious vaccine could become available. Timely initiation of measures and school closure play important roles. Because of the current lack of data on which to base such models, further field research is recommended to learn more about the sources of transmission and the effectiveness of social distancing measures in reducing influenza transmission.


Url:
DOI: 10.1073/pnas.0706849105
PubMed: 18332436
PubMed Central: 2290797

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PMC:2290797

Le document en format XML

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<front>
<div type="abstract" xml:lang="en">
<p>Planning a response to an outbreak of a pandemic strain of influenza is a high public health priority. Three research groups using different individual-based, stochastic simulation models have examined the consequences of intervention strategies chosen in consultation with U.S. public health workers. The first goal is to simulate the effectiveness of a set of potentially feasible intervention strategies. Combinations called targeted layered containment (TLC) of influenza antiviral treatment and prophylaxis and nonpharmaceutical interventions of quarantine, isolation, school closure, community social distancing, and workplace social distancing are considered. The second goal is to examine the robustness of the results to model assumptions. The comparisons focus on a pandemic outbreak in a population similar to that of Chicago, with ≈8.6 million people. The simulations suggest that at the expected transmissibility of a pandemic strain, timely implementation of a combination of targeted household antiviral prophylaxis, and social distancing measures could substantially lower the illness attack rate before a highly efficacious vaccine could become available. Timely initiation of measures and school closure play important roles. Because of the current lack of data on which to base such models, further field research is recommended to learn more about the sources of transmission and the effectiveness of social distancing measures in reducing influenza transmission.</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="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">18332436</article-id>
<article-id pub-id-type="pmc">2290797</article-id>
<article-id pub-id-type="publisher-id">9611</article-id>
<article-id pub-id-type="doi">10.1073/pnas.0706849105</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Social Sciences</subject>
<subj-group>
<subject>Environmental Sciences</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Modeling targeted layered containment of an influenza pandemic in the United States</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Halloran</surname>
<given-names>M. Elizabeth</given-names>
</name>
<xref ref-type="aff" rid="aff6">*</xref>
<xref ref-type="aff" rid="aff7">
<sup></sup>
</xref>
<xref ref-type="corresp" rid="cor1">
<sup></sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ferguson</surname>
<given-names>Neil M.</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>§</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Eubank</surname>
<given-names>Stephen</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup></sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Longini</surname>
<given-names>Ira M.</given-names>
<suffix>Jr.</suffix>
</name>
<xref ref-type="aff" rid="aff6">*</xref>
<xref ref-type="aff" rid="aff7">
<sup></sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Cummings</surname>
<given-names>Derek A. T.</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>§</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Lewis</surname>
<given-names>Bryan</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup></sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Xu</surname>
<given-names>Shufu</given-names>
</name>
<xref ref-type="aff" rid="aff7">
<sup></sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Fraser</surname>
<given-names>Christophe</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>§</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Vullikanti</surname>
<given-names>Anil</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup></sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Germann</surname>
<given-names>Timothy C.</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup></sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wagener</surname>
<given-names>Diane</given-names>
</name>
<xref ref-type="aff" rid="aff3">**</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Beckman</surname>
<given-names>Richard</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup></sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Kadau</surname>
<given-names>Kai</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup></sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Barrett</surname>
<given-names>Chris</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup></sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Macken</surname>
<given-names>Catherine A.</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup></sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Burke</surname>
<given-names>Donald S.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>††</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Cooley</surname>
<given-names>Philip</given-names>
</name>
<xref ref-type="aff" rid="aff3">**</xref>
</contrib>
<aff id="aff1">
<sup></sup>
Virginia Bioinformatics Institute, Virginia Polytechnical Institute and State University, Blacksburg, VA 24061;</aff>
<aff id="aff2">
<sup>††</sup>
Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261;</aff>
<aff id="aff3">**Research Triangle Institute, Research Triangle Park, NC 27709;</aff>
<aff id="aff4">
<sup>§</sup>
Department of Infectious Disease Epidemiology, Imperial College, London W21PG, England;</aff>
<aff id="aff5">
<sup></sup>
Los Alamos National Laboratories, Los Alamos, NM 87545;</aff>
<aff id="aff6">*Department of Biostatistics, School of Public Health and Community Medicine, University of Washington, Seattle, WA 98195; and</aff>
<aff id="aff7">
<sup></sup>
Program in Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109</aff>
</contrib-group>
<author-notes>
<corresp id="cor1">
<sup></sup>
To whom correspondence should be addressed. E-mail:
<email>betz@u.washington.edu</email>
</corresp>
<fn fn-type="edited-by">
<p>Edited by Barry R. Bloom, Harvard School of Public Health, Boston, MA, and approved January 15, 2008</p>
</fn>
<fn fn-type="con">
<p>Author contributions: M.E.H., I.M.L., S.X., and C.A.M. designed research; M.E.H., N.M.F., S.E., D.A.T.C., B.L., S.X., C.F., A.V., T.C.G., R.B., K.K., and C.B. performed research; M.E.H., N.M.F., S.E., I.M.L., D.W., and P.C. analyzed data; and M.E.H., N.M.F., I.M.L., and D.S.B. wrote the paper.</p>
</fn>
<fn fn-type="conflict">
<p>The authors declare no conflict of interest.</p>
</fn>
</author-notes>
<pub-date pub-type="ppub">
<day>25</day>
<month>3</month>
<year>2008</year>
</pub-date>
<pub-date pub-type="epub">
<day>10</day>
<month>3</month>
<year>2008</year>
</pub-date>
<pub-date pub-type="pmc-release">
<day>10</day>
<month>3</month>
<year>2008</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on the epub date downloaded from Highwire. </pmc-comment>
<volume>105</volume>
<issue>12</issue>
<fpage>4639</fpage>
<lpage>4644</lpage>
<history>
<date date-type="received">
<day>23</day>
<month>7</month>
<year>2007</year>
</date>
</history>
<permissions>
<copyright-statement>© 2008 by The National Academy of Sciences of the USA</copyright-statement>
<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:type="simple" xlink:href="zpq01208004639.pdf"></self-uri>
<abstract>
<p>Planning a response to an outbreak of a pandemic strain of influenza is a high public health priority. Three research groups using different individual-based, stochastic simulation models have examined the consequences of intervention strategies chosen in consultation with U.S. public health workers. The first goal is to simulate the effectiveness of a set of potentially feasible intervention strategies. Combinations called targeted layered containment (TLC) of influenza antiviral treatment and prophylaxis and nonpharmaceutical interventions of quarantine, isolation, school closure, community social distancing, and workplace social distancing are considered. The second goal is to examine the robustness of the results to model assumptions. The comparisons focus on a pandemic outbreak in a population similar to that of Chicago, with ≈8.6 million people. The simulations suggest that at the expected transmissibility of a pandemic strain, timely implementation of a combination of targeted household antiviral prophylaxis, and social distancing measures could substantially lower the illness attack rate before a highly efficacious vaccine could become available. Timely initiation of measures and school closure play important roles. Because of the current lack of data on which to base such models, further field research is recommended to learn more about the sources of transmission and the effectiveness of social distancing measures in reducing influenza transmission.</p>
</abstract>
<kwd-group>
<kwd>influenza antiviral agents</kwd>
<kwd>mitigation</kwd>
<kwd>prophylaxis</kwd>
<kwd>social distancing</kwd>
<kwd>transmission</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
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