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 : 001C39 ( Main/Exploration ); précédent : 001C38; suivant : 001C40The 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 [Japon, Hong Kong] ; Benjamin J. Cowling [Hong Kong] ; Kazuyuki Aihara [Japon] ; Hiroshi NishiuraSource :
- Computational and Mathematical Methods in Medicine [ 1748-670X ] ; 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.
Url:
DOI: 10.1155/2012/978901
PubMed: 22649483
PubMed Central: 3357941
Affiliations:
- Hong Kong, Japon
- Kyūshū, Préfecture de Fukuoka, Région de Kantō
- Fukuoka, Tokyo
- Université de Kyūshū, Université de Tokyo
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Le document en format XML
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sortKey="Nishiura, Hiroshi" sort="Nishiura, Hiroshi" uniqKey="Nishiura H" first="Hiroshi" last="Nishiura">Hiroshi Nishiura</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author></analytic><series><title level="j">Computational and Mathematical Methods in Medicine</title><idno type="ISSN">1748-670X</idno><idno type="eISSN">1748-6718</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Communicable Diseases, Emerging (mortality)</term><term>Communicable Diseases, Emerging (transmission)</term><term>Computer Simulation</term><term>Humans</term><term>Influenza, Human (mortality)</term><term>Influenza, Human (transmission)</term><term>Influenza, Human (virology)</term><term>Likelihood Functions</term><term>Models, Biological</term><term>Monte Carlo Method</term><term>Mortality</term><term>Orthomyxoviridae (pathogenicity)</term><term>Pandemics (statistics & numerical data)</term><term>Reproducibility of Results</term><term>Time Factors</term><term>Virulence</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Facteurs temps</term><term>Fonctions de vraisemblance</term><term>Grippe humaine (mortalité)</term><term>Grippe humaine (transmission)</term><term>Grippe humaine (virologie)</term><term>Humains</term><term>Maladies transmissibles émergentes (mortalité)</term><term>Maladies transmissibles émergentes (transmission)</term><term>Modèles biologiques</term><term>Mortalité</term><term>Méthode de Monte-Carlo</term><term>Orthomyxoviridae (pathogénicité)</term><term>Pandémies ()</term><term>Reproductibilité des résultats</term><term>Simulation numérique</term><term>Virulence</term></keywords><keywords scheme="MESH" qualifier="mortality" xml:lang="en"><term>Communicable Diseases, Emerging</term><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="mortalité" xml:lang="fr"><term>Grippe humaine</term><term>Maladies transmissibles émergentes</term></keywords><keywords scheme="MESH" qualifier="pathogenicity" xml:lang="en"><term>Orthomyxoviridae</term></keywords><keywords scheme="MESH" qualifier="pathogénicité" xml:lang="fr"><term>Orthomyxoviridae</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Pandemics</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Communicable Diseases, Emerging</term><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Computer Simulation</term><term>Humans</term><term>Likelihood Functions</term><term>Models, Biological</term><term>Monte Carlo Method</term><term>Mortality</term><term>Reproducibility of Results</term><term>Time Factors</term><term>Virulence</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Facteurs temps</term><term>Fonctions de vraisemblance</term><term>Grippe humaine</term><term>Humains</term><term>Maladies transmissibles émergentes</term><term>Modèles biologiques</term><term>Mortalité</term><term>Méthode de Monte-Carlo</term><term>Pandémies</term><term>Reproductibilité des résultats</term><term>Simulation numérique</term><term>Virulence</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Ma, J" uniqKey="Ma J">J Ma</name></author><author><name sortKey="Van Den Driessche, P" uniqKey="Van Den Driessche P">P Van Den Driessche</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Nishiura, H" uniqKey="Nishiura H">H Nishiura</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Lipsitch, M" uniqKey="Lipsitch M">M Lipsitch</name></author><author><name sortKey="Riley, S" uniqKey="Riley S">S Riley</name></author><author><name sortKey="Cauchemez, S" uniqKey="Cauchemez S">S Cauchemez</name></author><author><name sortKey="Ghani, Ac" uniqKey="Ghani A">AC 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N" uniqKey="Thompson N">N Thompson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chowell, G" uniqKey="Chowell G">G Chowell</name></author><author><name sortKey="Bettencourt, Lma" uniqKey="Bettencourt L">LMA Bettencourt</name></author><author><name sortKey="Johnson, N" uniqKey="Johnson N">N Johnson</name></author><author><name sortKey="Alonso, Wj" uniqKey="Alonso W">WJ Alonso</name></author><author><name sortKey="Viboud, C" uniqKey="Viboud C">C Viboud</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Richard, Sa" uniqKey="Richard S">SA Richard</name></author><author><name sortKey="Sugaya, N" uniqKey="Sugaya N">N Sugaya</name></author><author><name sortKey="Simonsen, L" uniqKey="Simonsen L">L Simonsen</name></author><author><name sortKey="Miller, Ma" uniqKey="Miller M">MA Miller</name></author><author><name sortKey="Viboud, C" uniqKey="Viboud C">C Viboud</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Yang, Y" uniqKey="Yang Y">Y Yang</name></author><author><name sortKey="Sugimoto, Jd" uniqKey="Sugimoto J">JD Sugimoto</name></author><author><name sortKey="Elizabeth Halloran, M" uniqKey="Elizabeth Halloran M">M Elizabeth Halloran</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Hong Kong</li><li>Japon</li></country><region><li>Kyūshū</li><li>Préfecture de Fukuoka</li><li>Région de Kantō</li></region><settlement><li>Fukuoka</li><li>Tokyo</li></settlement><orgName><li>Université de Kyūshū</li><li>Université de Tokyo</li></orgName></list><tree><noCountry><name sortKey="Nishiura, Hiroshi" sort="Nishiura, Hiroshi" uniqKey="Nishiura H" first="Hiroshi" last="Nishiura">Hiroshi Nishiura</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><name sortKey="Aihara, Kazuyuki" sort="Aihara, Kazuyuki" uniqKey="Aihara K" first="Kazuyuki" last="Aihara">Kazuyuki Aihara</name><name sortKey="Aihara, Kazuyuki" sort="Aihara, Kazuyuki" uniqKey="Aihara K" first="Kazuyuki" last="Aihara">Kazuyuki Aihara</name><name sortKey="Omori, Ryosuke" sort="Omori, Ryosuke" uniqKey="Omori R" first="Ryosuke" last="Omori">Ryosuke Omori</name></country><country name="Hong Kong"><noRegion><name sortKey="Omori, Ryosuke" sort="Omori, Ryosuke" uniqKey="Omori R" first="Ryosuke" last="Omori">Ryosuke Omori</name></noRegion><name sortKey="Cowling, Benjamin J" sort="Cowling, Benjamin J" uniqKey="Cowling B" first="Benjamin J." last="Cowling">Benjamin J. 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