Sensitivity Analysis of an Individual-Based Model for Simulation of Influenza Epidemics
Identifieur interne : 001A61 ( Main/Merge ); précédent : 001A60; suivant : 001A62Sensitivity 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.
Descripteurs français
- KwdFr :
- Algorithmes, Facteurs temps, Grippe humaine (transmission), Grippe humaine (épidémiologie), Humains, Modèles théoriques, New York (ville) (épidémiologie), Processus stochastiques, Reproductibilité des résultats, Santé en zone urbaine, Santé en zone rurale, Santé publique, Simulation numérique, Virginie (épidémiologie), Épidémies.
- MESH :
English descriptors
- KwdEn :
- Algorithms, Computer Simulation, Epidemics, Humans, Influenza, Human (epidemiology), Influenza, Human (transmission), Models, Theoretical, New York City (epidemiology), Public Health, Reproducibility of Results, Rural Health, Stochastic Processes, Time Factors, Urban Health, Virginia (epidemiology).
- MESH :
- geographic , epidemiology : New York City, Virginia.
- epidemiology : Influenza, Human.
- transmission : Influenza, Human.
- Algorithms, Computer Simulation, Epidemics, Humans, Models, Theoretical, Public Health, Reproducibility of Results, Rural Health, Stochastic Processes, Time Factors, Urban Health.
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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PMC:3483224Le document en format XML
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Nsoesie</name><affiliation wicri:level="2"><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><placeName><region type="state">Virginie</region></placeName></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="2"><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><placeName><region type="state">Virginie</region></placeName></affiliation></author><author><name sortKey="Marathe, Madhav V" sort="Marathe, Madhav V" uniqKey="Marathe M" first="Madhav V." last="Marathe">Madhav V. 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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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Rahmandad</name></author><author><name sortKey="Sterman, J" uniqKey="Sterman J">J Sterman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cowling, Bj" uniqKey="Cowling B">BJ Cowling</name></author><author><name sortKey="Chan, Kh" uniqKey="Chan K">KH Chan</name></author><author><name sortKey="Fang, Vj" uniqKey="Fang V">VJ Fang</name></author><author><name sortKey="Lau, Llh" uniqKey="Lau L">LLH Lau</name></author><author><name sortKey="So, Hc" uniqKey="So H">HC So</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cauchemez, S" uniqKey="Cauchemez S">S Cauchemez</name></author><author><name sortKey="Donnelly, Ca" uniqKey="Donnelly C">CA Donnelly</name></author><author><name sortKey="Reed, C" uniqKey="Reed C">C Reed</name></author><author><name sortKey="Ghani, Ac" uniqKey="Ghani A">AC Ghani</name></author><author><name sortKey="Fraser, C" uniqKey="Fraser C">C Fraser</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chao, Dl" uniqKey="Chao D">DL Chao</name></author><author><name sortKey="Halloran, Me" uniqKey="Halloran M">ME Halloran</name></author><author><name sortKey="Longini, Im" uniqKey="Longini I">IM Longini</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Keeling, Mj" uniqKey="Keeling M">MJ Keeling</name></author><author><name sortKey="Eames, Kt" uniqKey="Eames K">KT Eames</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pautasso, M" uniqKey="Pautasso M">M Pautasso</name></author><author><name sortKey="Jeger, Mj" uniqKey="Jeger M">MJ Jeger</name></author></analytic></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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