Sensitivity Analysis of an Individual-Based Model for Simulation of Influenza Epidemics
Identifieur interne : 000D56 ( Pmc/Curation ); précédent : 000D55; suivant : 000D57Sensitivity Analysis of an Individual-Based Model for Simulation of Influenza Epidemics
Auteurs : Elaine O. Nsoesie [États-Unis] ; Richard J. Beckman [États-Unis] ; Madhav V. Marathe [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2012.
Abstract
Individual-based epidemiology models are increasingly used in the study of influenza epidemics. Several studies on influenza dynamics and evaluation of intervention measures have used the same incubation and infectious period distribution parameters based on the natural history of influenza. A sensitivity analysis evaluating the influence of slight changes to these parameters (in addition to the transmissibility) would be useful for future studies and real-time modeling during an influenza pandemic.
In this study, we examined individual and joint effects of parameters and ranked parameters based on their influence on the dynamics of simulated epidemics. We also compared the sensitivity of the model across synthetic social networks for Montgomery County in Virginia and New York City (and surrounding metropolitan regions) with demographic and rural-urban differences. In addition, we studied the effects of changing the mean infectious period on age-specific epidemics. The research was performed from a public health standpoint using three relevant measures: time to peak, peak infected proportion and total attack rate. We also used statistical methods in the design and analysis of the experiments.
The results showed that: (i) minute changes in the transmissibility and mean infectious period significantly influenced the attack rate; (ii) the mean of the incubation period distribution appeared to be sufficient for determining its effects on the dynamics of epidemics; (iii) the infectious period distribution had the strongest influence on the structure of the epidemic curves; (iv) the sensitivity of the individual-based model was consistent across social networks investigated in this study and (v) age-specific epidemics were sensitive to changes in the mean infectious period irrespective of the susceptibility of the other age groups. These findings suggest that small changes in some of the disease model parameters can significantly influence the uncertainty observed in real-time forecasting and predicting of the characteristics of an epidemic.
Url:
DOI: 10.1371/journal.pone.0045414
PubMed: 23144693
PubMed Central: 3483224
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Sensitivity Analysis of an Individual-Based Model for Simulation of Influenza Epidemics</title><author><name sortKey="Nsoesie, Elaine O" sort="Nsoesie, Elaine O" uniqKey="Nsoesie E" first="Elaine O." last="Nsoesie">Elaine O. Nsoesie</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia</wicri:regionArea></affiliation></author><author><name sortKey="Beckman, Richard J" sort="Beckman, Richard J" uniqKey="Beckman R" first="Richard J." last="Beckman">Richard J. Beckman</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia</wicri:regionArea></affiliation></author><author><name sortKey="Marathe, Madhav V" sort="Marathe, Madhav V" uniqKey="Marathe M" first="Madhav V." last="Marathe">Madhav V. Marathe</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, Virginia Tech, Blacksburg, Virginia</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">23144693</idno><idno type="pmc">3483224</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3483224</idno><idno type="RBID">PMC:3483224</idno><idno type="doi">10.1371/journal.pone.0045414</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000D56</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000D56</idno><idno type="wicri:Area/Pmc/Curation">000D56</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000D56</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Sensitivity Analysis of an Individual-Based Model for Simulation of Influenza Epidemics</title><author><name sortKey="Nsoesie, Elaine O" sort="Nsoesie, Elaine O" uniqKey="Nsoesie E" first="Elaine O." last="Nsoesie">Elaine O. Nsoesie</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia</wicri:regionArea></affiliation></author><author><name sortKey="Beckman, Richard J" sort="Beckman, Richard J" uniqKey="Beckman R" first="Richard J." last="Beckman">Richard J. Beckman</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia</wicri:regionArea></affiliation></author><author><name sortKey="Marathe, Madhav V" sort="Marathe, Madhav V" uniqKey="Marathe M" first="Madhav V." last="Marathe">Madhav V. Marathe</name><affiliation wicri:level="1"><nlm:aff id="aff1"><addr-line>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, Virginia Tech, Blacksburg, Virginia</wicri:regionArea></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Individual-based epidemiology models are increasingly used in the study of influenza epidemics. Several studies on influenza dynamics and evaluation of intervention measures have used the same incubation and infectious period distribution parameters based on the natural history of influenza. A sensitivity analysis evaluating the influence of slight changes to these parameters (in addition to the transmissibility) would be useful for future studies and real-time modeling during an influenza pandemic.</p><p>In this study, we examined individual and joint effects of parameters and ranked parameters based on their influence on the dynamics of simulated epidemics. We also compared the sensitivity of the model across synthetic social networks for Montgomery County in Virginia and New York City (and surrounding metropolitan regions) with demographic and rural-urban differences. In addition, we studied the effects of changing the mean infectious period on age-specific epidemics. The research was performed from a public health standpoint using three relevant measures: time to peak, peak infected proportion and total attack rate. We also used statistical methods in the design and analysis of the experiments.</p><p>The results showed that: (i) minute changes in the transmissibility and mean infectious period significantly influenced the attack rate; (ii) the mean of the incubation period distribution appeared to be sufficient for determining its effects on the dynamics of epidemics; (iii) the infectious period distribution had the strongest influence on the structure of the epidemic curves; (iv) the sensitivity of the individual-based model was consistent across social networks investigated in this study and (v) age-specific epidemics were sensitive to changes in the mean infectious period irrespective of the susceptibility of the other age groups. These findings suggest that small changes in some of the disease model parameters can significantly influence the uncertainty observed in real-time forecasting and predicting of the characteristics of an epidemic.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Saltelli, A" uniqKey="Saltelli A">A Saltelli</name></author><author><name sortKey="Annoni, P" uniqKey="Annoni P">P Annoni</name></author><author><name sortKey="Azzini, I" uniqKey="Azzini I">I Azzini</name></author><author><name sortKey="Campolongo, F" uniqKey="Campolongo F">F Campolongo</name></author><author><name sortKey="Ratto, M" uniqKey="Ratto M">M Ratto</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Ginot, V" uniqKey="Ginot V">V Ginot</name></author><author><name sortKey="Gaba, S" uniqKey="Gaba S">S Gaba</name></author><author><name sortKey="Beaudouin, R" uniqKey="Beaudouin R">R 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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id><journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosone</journal-id><journal-title-group><journal-title>PLoS ONE</journal-title></journal-title-group><issn pub-type="epub">1932-6203</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">23144693</article-id><article-id pub-id-type="pmc">3483224</article-id><article-id pub-id-type="publisher-id">PONE-D-12-01701</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0045414</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Biology</subject><subj-group><subject>Computational Biology</subject><subj-group><subject>Population Modeling</subject><subj-group><subject>Infectious Disease Modeling</subject></subj-group></subj-group></subj-group><subj-group><subject>Population Biology</subject><subj-group><subject>Epidemiology</subject><subj-group><subject>Infectious Disease Epidemiology</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Computer Science</subject><subj-group><subject>Computer Modeling</subject></subj-group><subj-group><subject>Computerized Simulations</subject></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Mathematics</subject><subj-group><subject>Statistics</subject><subj-group><subject>Biostatistics</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Medicine</subject><subj-group><subject>Epidemiology</subject><subj-group><subject>Infectious Disease Epidemiology</subject></subj-group></subj-group><subj-group><subject>Infectious Diseases</subject><subj-group><subject>Viral Diseases</subject><subj-group><subject>Influenza</subject></subj-group></subj-group></subj-group><subj-group><subject>Public Health</subject></subj-group></subj-group></article-categories><title-group><article-title>Sensitivity Analysis of an Individual-Based Model for Simulation of Influenza Epidemics</article-title><alt-title alt-title-type="running-head">Sensitivity Analysis of an Individual-Based Model</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Nsoesie</surname><given-names>Elaine O.</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>Beckman</surname><given-names>Richard J.</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Marathe</surname><given-names>Madhav V.</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-group><aff id="aff1"><label>1</label><addr-line>Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line></aff><aff id="aff2"><label>2</label><addr-line>Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Vespignani</surname><given-names>Alessandro</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1"><addr-line>Northeastern University, United States of America</addr-line></aff><author-notes><corresp id="cor1">* E-mail: <email>onelaine@vt.edu</email></corresp><fn fn-type="COI-statement"><p><bold>Competing Interests: </bold>The authors have declared that no competing interests exist.</p></fn><fn fn-type="con"><p>Conceived and designed the experiments: EON RJB MVM. Performed the experiments: EON RJB MVM. Analyzed the data: EON RJB MVM. Contributed reagents/materials/analysis tools: EON RJB MVM. Wrote the paper: EON RJB MVM.</p></fn></author-notes><pub-date pub-type="collection"><year>2012</year></pub-date><pub-date pub-type="epub"><day>29</day><month>10</month><year>2012</year></pub-date><volume>7</volume><issue>10</issue><elocation-id>e45414</elocation-id><history><date date-type="received"><day>16</day><month>1</month><year>2012</year></date><date date-type="accepted"><day>22</day><month>8</month><year>2012</year></date></history><permissions><copyright-year>2012</copyright-year><license xlink:href="https://creativecommons.org/publicdomain/zero/1.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.</license-p></license></permissions><abstract><p>Individual-based epidemiology models are increasingly used in the study of influenza epidemics. Several studies on influenza dynamics and evaluation of intervention measures have used the same incubation and infectious period distribution parameters based on the natural history of influenza. A sensitivity analysis evaluating the influence of slight changes to these parameters (in addition to the transmissibility) would be useful for future studies and real-time modeling during an influenza pandemic.</p><p>In this study, we examined individual and joint effects of parameters and ranked parameters based on their influence on the dynamics of simulated epidemics. We also compared the sensitivity of the model across synthetic social networks for Montgomery County in Virginia and New York City (and surrounding metropolitan regions) with demographic and rural-urban differences. In addition, we studied the effects of changing the mean infectious period on age-specific epidemics. The research was performed from a public health standpoint using three relevant measures: time to peak, peak infected proportion and total attack rate. We also used statistical methods in the design and analysis of the experiments.</p><p>The results showed that: (i) minute changes in the transmissibility and mean infectious period significantly influenced the attack rate; (ii) the mean of the incubation period distribution appeared to be sufficient for determining its effects on the dynamics of epidemics; (iii) the infectious period distribution had the strongest influence on the structure of the epidemic curves; (iv) the sensitivity of the individual-based model was consistent across social networks investigated in this study and (v) age-specific epidemics were sensitive to changes in the mean infectious period irrespective of the susceptibility of the other age groups. These findings suggest that small changes in some of the disease model parameters can significantly influence the uncertainty observed in real-time forecasting and predicting of the characteristics of an epidemic.</p></abstract><funding-group><funding-statement>This work has been partially supported by National Science Foundation Netse Grant CNS-1011769, Defense Threat Reduction Agency (DTRA) R&D Grant HDTRA1-0901-0017, DTRA Comprehensive National Incident Management System (CNIMS) Grant HDTRA1-07-C-0113, National Institutes of Health Models of Infectious Disease Study (MIDAS) project 2U01GM070694-7 and DTRA Rigorous Approaches for Validation and Verification of Networked Systems Grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement></funding-group><counts><page-count count="16"></page-count></counts></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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