The time required to estimate the case fatality ratio of influenza using only the tip of an iceberg: joint estimation of the virulence and the transmission potential.
Identifieur interne : 001261 ( PubMed/Checkpoint ); précédent : 001260; suivant : 001262The time required to estimate the case fatality ratio of influenza using only the tip of an iceberg: joint estimation of the virulence and the transmission potential.
Auteurs : Keisuke Ejima [Japon] ; Ryosuke Omori ; Benjamin J. Cowling ; Kazuyuki Aihara ; Hiroshi NishiuraSource :
- Computational and mathematical methods in medicine [ 1748-6718 ] ; 2012.
Descripteurs français
- KwdFr :
- Facteurs temps, Fonctions de vraisemblance, Grippe humaine (mortalité), Grippe humaine (transmission), Grippe humaine (virologie), Humains, Maladies transmissibles émergentes (mortalité), Maladies transmissibles émergentes (transmission), Modèles biologiques, Mortalité, Méthode de Monte-Carlo, Orthomyxoviridae (pathogénicité), Pandémies (), Reproductibilité des résultats, Simulation numérique, Virulence.
- MESH :
- mortalité : Grippe humaine, Maladies transmissibles émergentes.
- pathogénicité : Orthomyxoviridae.
- virologie : Grippe humaine.
- Facteurs temps, Fonctions de vraisemblance, Grippe humaine, Humains, Maladies transmissibles émergentes, Modèles biologiques, Mortalité, Méthode de Monte-Carlo, Pandémies, Reproductibilité des résultats, Simulation numérique, Virulence.
English descriptors
- KwdEn :
- Communicable Diseases, Emerging (mortality), Communicable Diseases, Emerging (transmission), Computer Simulation, Humans, Influenza, Human (mortality), Influenza, Human (transmission), Influenza, Human (virology), Likelihood Functions, Models, Biological, Monte Carlo Method, Mortality, Orthomyxoviridae (pathogenicity), Pandemics (statistics & numerical data), Reproducibility of Results, Time Factors, Virulence.
- MESH :
- mortality : Communicable Diseases, Emerging, Influenza, Human.
- pathogenicity : Orthomyxoviridae.
- statistics & numerical data : Pandemics.
- transmission : Communicable Diseases, Emerging, Influenza, Human.
- virology : Influenza, Human.
- Computer Simulation, Humans, Likelihood Functions, Models, Biological, Monte Carlo Method, Mortality, Reproducibility of Results, Time Factors, Virulence.
Abstract
Estimating the case fatality ratio (CFR) of a novel strain of influenza virus during the early stage of the pandemic is one of key epidemiological tasks to be conducted as rapid research response. Past experience during the epidemics of severe acute respiratory syndrome (SARS) and influenza A (H1N1-2009) posed several technical challenges in estimating the CFR in real time. The present study aimed to develop a simple method to estimate the CFR based on readily available datasets, that is, confirmed cases and deaths, while addressing some of the known technical issues. To assess the reliability and validity of the proposed method, we examined the minimum length of time required for the assigned CFR to be included within the 95% confidence intervals and for the estimated CFR to be below a prespecified cut-off value by means of Monte Carlo simulations. Overall, the smaller the transmission potential was, the longer it took to compare the estimated CFR against the cut-off value. If policymaking and public health response have to be made based on the CFR estimate derived from the proposed method and readily available data, it should be noted that the successful estimation may take longer than a few months.
DOI: 10.1155/2012/978901
PubMed: 22649483
Affiliations:
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Past experience during the epidemics of severe acute respiratory syndrome (SARS) and influenza A (H1N1-2009) posed several technical challenges in estimating the CFR in real time. The present study aimed to develop a simple method to estimate the CFR based on readily available datasets, that is, confirmed cases and deaths, while addressing some of the known technical issues. To assess the reliability and validity of the proposed method, we examined the minimum length of time required for the assigned CFR to be included within the 95% confidence intervals and for the estimated CFR to be below a prespecified cut-off value by means of Monte Carlo simulations. Overall, the smaller the transmission potential was, the longer it took to compare the estimated CFR against the cut-off value. If policymaking and public health response have to be made based on the CFR estimate derived from the proposed method and readily available data, it should be noted that the successful estimation may take longer than a few months.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ejima</LastName><ForeName>Keisuke</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Omori</LastName><ForeName>Ryosuke</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Cowling</LastName><ForeName>Benjamin J</ForeName><Initials>BJ</Initials></Author><Author ValidYN="Y"><LastName>Aihara</LastName><ForeName>Kazuyuki</ForeName><Initials>K</Initials></Author><Author 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Kazuyuki" uniqKey="Aihara K" first="Kazuyuki" last="Aihara">Kazuyuki Aihara</name><name sortKey="Cowling, Benjamin J" sort="Cowling, Benjamin J" uniqKey="Cowling B" first="Benjamin J" last="Cowling">Benjamin J. Cowling</name><name sortKey="Nishiura, Hiroshi" sort="Nishiura, Hiroshi" uniqKey="Nishiura H" first="Hiroshi" last="Nishiura">Hiroshi Nishiura</name><name sortKey="Omori, Ryosuke" sort="Omori, Ryosuke" uniqKey="Omori R" first="Ryosuke" last="Omori">Ryosuke Omori</name></noCountry><country name="Japon"><region name="Région de Kantō"><name sortKey="Ejima, Keisuke" sort="Ejima, Keisuke" uniqKey="Ejima K" first="Keisuke" last="Ejima">Keisuke Ejima</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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