FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model
Identifieur interne : 000A42 ( Pmc/Curation ); précédent : 000A41; suivant : 000A43FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model
Auteurs : Dennis L. Chao [États-Unis] ; M. Elizabeth Halloran [États-Unis] ; Valerie J. Obenchain [États-Unis] ; Ira M. Longini [États-Unis]Source :
- PLoS Computational Biology [ 1553-734X ] ; 2010.
Abstract
Mathematical and computer models of epidemics have contributed to our understanding of the spread of infectious disease and the measures needed to contain or mitigate them. To help prepare for future influenza seasonal epidemics or pandemics, we developed a new stochastic model of the spread of influenza across a large population. Individuals in this model have realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza. The model has been calibrated so that outcomes are consistent with the 1957/1958 Asian A(H2N2) and 2009 pandemic A(H1N1) influenza viruses. We present examples of how this model can be used to study the dynamics of influenza epidemics in the United States and simulate how to mitigate or delay them using pharmaceutical interventions and social distancing measures. Computer simulation models play an essential role in informing public policy and evaluating pandemic preparedness plans. We have made the source code of this model publicly available to encourage its use and further development.
Url:
DOI: 10.1371/journal.pcbi.1000656
PubMed: 20126529
PubMed Central: 2813259
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Elizabeth Halloran</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Center for Statistics and Quantitative Infectious Diseases/Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Statistics and Quantitative Infectious Diseases/Fred Hutchinson Cancer Research Center, Seattle, Washington</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Department of Biostatistics, School of Public Health/University of Washington, Seattle, Washington, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biostatistics, School of Public Health/University of Washington, Seattle, Washington</wicri:regionArea></affiliation></author><author><name sortKey="Obenchain, Valerie J" sort="Obenchain, Valerie J" uniqKey="Obenchain V" first="Valerie J." last="Obenchain">Valerie J. 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Chao</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Center for Statistics and Quantitative Infectious Diseases/Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Statistics and Quantitative Infectious Diseases/Fred Hutchinson Cancer Research Center, Seattle, Washington</wicri:regionArea></affiliation></author><author><name sortKey="Halloran, M Elizabeth" sort="Halloran, M Elizabeth" uniqKey="Halloran M" first="M. Elizabeth" last="Halloran">M. Elizabeth Halloran</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Center for Statistics and Quantitative Infectious Diseases/Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Statistics and Quantitative Infectious Diseases/Fred Hutchinson Cancer Research Center, Seattle, Washington</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Department of Biostatistics, School of Public Health/University of Washington, Seattle, Washington, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biostatistics, School of Public Health/University of Washington, Seattle, Washington</wicri:regionArea></affiliation></author><author><name sortKey="Obenchain, Valerie J" sort="Obenchain, Valerie J" uniqKey="Obenchain V" first="Valerie J." last="Obenchain">Valerie J. Obenchain</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Center for Statistics and Quantitative Infectious Diseases/Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Statistics and Quantitative Infectious Diseases/Fred Hutchinson Cancer Research Center, Seattle, Washington</wicri:regionArea></affiliation></author><author><name sortKey="Longini, Ira M" sort="Longini, Ira M" uniqKey="Longini I" first="Ira M." last="Longini">Ira M. Longini</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Center for Statistics and Quantitative Infectious Diseases/Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Statistics and Quantitative Infectious Diseases/Fred Hutchinson Cancer Research Center, Seattle, Washington</wicri:regionArea></affiliation></author></analytic><series><title level="j">PLoS Computational Biology</title><idno type="ISSN">1553-734X</idno><idno type="eISSN">1553-7358</idno><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Mathematical and computer models of epidemics have contributed to our understanding of the spread of infectious disease and the measures needed to contain or mitigate them. To help prepare for future influenza seasonal epidemics or pandemics, we developed a new stochastic model of the spread of influenza across a large population. Individuals in this model have realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza. The model has been calibrated so that outcomes are consistent with the 1957/1958 Asian A(H2N2) and 2009 pandemic A(H1N1) influenza viruses. We present examples of how this model can be used to study the dynamics of influenza epidemics in the United States and simulate how to mitigate or delay them using pharmaceutical interventions and social distancing measures. Computer simulation models play an essential role in informing public policy and evaluating pandemic preparedness plans. 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Ackerman</name></author><author><name sortKey="Elveback, Lr" uniqKey="Elveback L">LR Elveback</name></author></analytic></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS Comput Biol</journal-id><journal-id journal-id-type="iso-abbrev">PLoS Comput. Biol</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">ploscomp</journal-id><journal-title-group><journal-title>PLoS Computational Biology</journal-title></journal-title-group><issn pub-type="ppub">1553-734X</issn><issn pub-type="epub">1553-7358</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">20126529</article-id><article-id pub-id-type="pmc">2813259</article-id><article-id pub-id-type="publisher-id">09-PLCB-RA-0847R2</article-id><article-id pub-id-type="doi">10.1371/journal.pcbi.1000656</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline"><subject>Computational Biology</subject><subject>Infectious Diseases/Epidemiology and Control of Infectious Diseases</subject><subject>Public Health and Epidemiology/Epidemiology</subject><subject>Public Health and Epidemiology/Infectious Diseases</subject></subj-group></article-categories><title-group><article-title>FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model</article-title><alt-title alt-title-type="running-head">Influenza Epidemic Simulation</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Chao</surname><given-names>Dennis L.</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Halloran</surname><given-names>M. Elizabeth</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Obenchain</surname><given-names>Valerie J.</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</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="aff1"><sup>1</sup></xref></contrib></contrib-group><aff id="aff1"><label>1</label><addr-line>Center for Statistics and Quantitative Infectious Diseases/Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America</addr-line></aff><aff id="aff2"><label>2</label><addr-line>Department of Biostatistics, School of Public Health/University of Washington, Seattle, Washington, United States of America</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>McLean</surname><given-names>Angela R.</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1">University of Oxford, United Kingdom</aff><author-notes><corresp id="cor1">* E-mail: <email>dchao@fhcrc.org</email></corresp><fn fn-type="con"><p>Conceived and designed the experiments: DLC MEH IMLJ. Performed the experiments: DLC. Analyzed the data: DLC. Contributed reagents/materials/analysis tools: DLC VJO. Wrote the paper: DLC MEH VJO IMLJ.</p></fn></author-notes><pub-date pub-type="collection"><month>1</month><year>2010</year></pub-date><pub-date pub-type="epub"><day>29</day><month>1</month><year>2010</year></pub-date><volume>6</volume><issue>1</issue><elocation-id>e1000656</elocation-id><history><date date-type="received"><day>17</day><month>7</month><year>2009</year></date><date date-type="accepted"><day>22</day><month>12</month><year>2009</year></date></history><permissions><copyright-statement>Chao et al.</copyright-statement><copyright-year>2010</copyright-year><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.</license-p></license></permissions><abstract><p>Mathematical and computer models of epidemics have contributed to our understanding of the spread of infectious disease and the measures needed to contain or mitigate them. To help prepare for future influenza seasonal epidemics or pandemics, we developed a new stochastic model of the spread of influenza across a large population. Individuals in this model have realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza. The model has been calibrated so that outcomes are consistent with the 1957/1958 Asian A(H2N2) and 2009 pandemic A(H1N1) influenza viruses. We present examples of how this model can be used to study the dynamics of influenza epidemics in the United States and simulate how to mitigate or delay them using pharmaceutical interventions and social distancing measures. Computer simulation models play an essential role in informing public policy and evaluating pandemic preparedness plans. We have made the source code of this model publicly available to encourage its use and further development.</p></abstract><abstract abstract-type="summary"><title>Author Summary</title><p>Computer simulations can provide valuable information to communities preparing for epidemics. These simulations can be used to investigate the effectiveness of various intervention strategies in reducing or delaying the peak of an epidemic. We have made a detailed influenza epidemic simulator for the United States publicly available so that others may use the software to inform public policy or adapt it to suit their needs.</p></abstract><counts><page-count count="8"></page-count></counts></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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