Model-based reconstruction of an epidemic using multiple datasets: understanding influenza A/H1N1 pandemic dynamics in Israel.
Identifieur interne : 000541 ( PubMed/Curation ); précédent : 000540; suivant : 000542Model-based reconstruction of an epidemic using multiple datasets: understanding influenza A/H1N1 pandemic dynamics in Israel.
Auteurs : R. Yaari [Israël] ; G. Katriel [Israël] ; L. Stone [Australie] ; E. Mendelson [Israël] ; M. Mandelboim [Israël] ; A. Huppert [Israël]Source :
- Journal of the Royal Society, Interface [ 1742-5662 ] ; 2016.
Descripteurs français
- KwdFr :
- Adolescent, Adulte, Adulte d'âge moyen, Bases de données factuelles, Enfant, Enfant d'âge préscolaire, Femelle, Grippe humaine (), Grippe humaine (épidémiologie), Humains, Modèles biologiques, Mâle, Nourrisson, Nouveau-né, Pandémies, Sous-type H1N1 du virus de la grippe A, Sujet âgé, Sujet âgé de 80 ans ou plus, Vaccination.
- MESH :
- épidémiologie : Grippe humaine.
- Adolescent, Adulte, Adulte d'âge moyen, Bases de données factuelles, Enfant, Enfant d'âge préscolaire, Femelle, Grippe humaine, Humains, Modèles biologiques, Mâle, Nourrisson, Nouveau-né, Pandémies, Sous-type H1N1 du virus de la grippe A, Sujet âgé, Sujet âgé de 80 ans ou plus, Vaccination.
English descriptors
- KwdEn :
- MESH :
- epidemiology : Influenza, Human.
- prevention & control : Influenza, Human.
- Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Databases, Factual, Female, Humans, Infant, Infant, Newborn, Influenza A Virus, H1N1 Subtype, Male, Middle Aged, Models, Biological, Pandemics, Vaccination.
Abstract
Intensified surveillance during the 2009 A/H1N1 influenza pandemic in Israel resulted in large virological and serological datasets, presenting a unique opportunity for investigating the pandemic dynamics. We employ a conditional likelihood approach for fitting a disease transmission model to virological and serological data, conditional on clinical data. The model is used to reconstruct the temporal pattern of the pandemic in Israel in five age-groups and evaluate the factors that shaped it. We estimate the reproductive number at the beginning of the pandemic to beR= 1.4. We find that the combined effect of varying absolute humidity conditions and school vacations (SVs) is responsible for the infection pattern, characterized by three epidemic waves. Overall attack rate is estimated at 32% (28-35%) with a large variation among the age-groups: the highest attack rates within school children and the lowest within the elderly. This pattern of infection is explained by a combination of the age-group contact structure and increasing immunity with age. We assess that SVs increased the overall attack rates by prolonging the pandemic into the winter. Vaccinating school children would have been the optimal strategy for minimizing infection rates in all age-groups.
DOI: 10.1098/rsif.2016.0099
PubMed: 27030041
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Huppert</name><affiliation wicri:level="1"><nlm:affiliation>Bio-statistical Unit, The Gertner Institute for Epidemiology and Health Policy Research, Chaim Sheba Medical Center, Tel-Hashomer 52621, Israel Sackler Faculty of Medicine, Tel-Aviv University, Ramat Aviv 69778, Israel amith@gertner.health.gov.il.</nlm:affiliation><country wicri:rule="url">Israël</country><wicri:regionArea>Bio-statistical Unit, The Gertner Institute for Epidemiology and Health Policy Research, Chaim Sheba Medical Center, Tel-Hashomer 52621, Israel Sackler Faculty of Medicine, Tel-Aviv University, Ramat Aviv 69778</wicri:regionArea></affiliation></author></analytic><series><title level="j">Journal of the Royal Society, Interface</title><idno type="eISSN">1742-5662</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Aged</term><term>Aged, 80 and over</term><term>Child</term><term>Child, Preschool</term><term>Databases, Factual</term><term>Female</term><term>Humans</term><term>Infant</term><term>Infant, Newborn</term><term>Influenza A Virus, H1N1 Subtype</term><term>Influenza, Human (epidemiology)</term><term>Influenza, Human (prevention & control)</term><term>Male</term><term>Middle Aged</term><term>Models, Biological</term><term>Pandemics</term><term>Vaccination</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Bases de données factuelles</term><term>Enfant</term><term>Enfant d'âge préscolaire</term><term>Femelle</term><term>Grippe humaine ()</term><term>Grippe humaine (épidémiologie)</term><term>Humains</term><term>Modèles biologiques</term><term>Mâle</term><term>Nourrisson</term><term>Nouveau-né</term><term>Pandémies</term><term>Sous-type H1N1 du virus de la grippe A</term><term>Sujet âgé</term><term>Sujet âgé de 80 ans ou plus</term><term>Vaccination</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Aged</term><term>Aged, 80 and over</term><term>Child</term><term>Child, Preschool</term><term>Databases, Factual</term><term>Female</term><term>Humans</term><term>Infant</term><term>Infant, Newborn</term><term>Influenza A Virus, H1N1 Subtype</term><term>Male</term><term>Middle Aged</term><term>Models, Biological</term><term>Pandemics</term><term>Vaccination</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Bases de données factuelles</term><term>Enfant</term><term>Enfant d'âge préscolaire</term><term>Femelle</term><term>Grippe humaine</term><term>Humains</term><term>Modèles biologiques</term><term>Mâle</term><term>Nourrisson</term><term>Nouveau-né</term><term>Pandémies</term><term>Sous-type H1N1 du virus de la grippe A</term><term>Sujet âgé</term><term>Sujet âgé de 80 ans ou plus</term><term>Vaccination</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Intensified surveillance during the 2009 A/H1N1 influenza pandemic in Israel resulted in large virological and serological datasets, presenting a unique opportunity for investigating the pandemic dynamics. We employ a conditional likelihood approach for fitting a disease transmission model to virological and serological data, conditional on clinical data. The model is used to reconstruct the temporal pattern of the pandemic in Israel in five age-groups and evaluate the factors that shaped it. We estimate the reproductive number at the beginning of the pandemic to beR= 1.4. We find that the combined effect of varying absolute humidity conditions and school vacations (SVs) is responsible for the infection pattern, characterized by three epidemic waves. Overall attack rate is estimated at 32% (28-35%) with a large variation among the age-groups: the highest attack rates within school children and the lowest within the elderly. This pattern of infection is explained by a combination of the age-group contact structure and increasing immunity with age. We assess that SVs increased the overall attack rates by prolonging the pandemic into the winter. Vaccinating school children would have been the optimal strategy for minimizing infection rates in all age-groups. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">27030041</PMID><DateCompleted><Year>2016</Year><Month>12</Month><Day>13</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1742-5662</ISSN><JournalIssue CitedMedium="Internet"><Volume>13</Volume><Issue>116</Issue><PubDate><Year>2016</Year><Month>Mar</Month></PubDate></JournalIssue><Title>Journal of the Royal Society, Interface</Title><ISOAbbreviation>J R Soc Interface</ISOAbbreviation></Journal><ArticleTitle>Model-based reconstruction of an epidemic using multiple datasets: understanding influenza A/H1N1 pandemic dynamics in Israel.</ArticleTitle><ELocationID EIdType="doi" ValidYN="Y">10.1098/rsif.2016.0099</ELocationID><ELocationID EIdType="pii" ValidYN="Y">20160099</ELocationID><Abstract><AbstractText>Intensified surveillance during the 2009 A/H1N1 influenza pandemic in Israel resulted in large virological and serological datasets, presenting a unique opportunity for investigating the pandemic dynamics. We employ a conditional likelihood approach for fitting a disease transmission model to virological and serological data, conditional on clinical data. The model is used to reconstruct the temporal pattern of the pandemic in Israel in five age-groups and evaluate the factors that shaped it. We estimate the reproductive number at the beginning of the pandemic to beR= 1.4. We find that the combined effect of varying absolute humidity conditions and school vacations (SVs) is responsible for the infection pattern, characterized by three epidemic waves. Overall attack rate is estimated at 32% (28-35%) with a large variation among the age-groups: the highest attack rates within school children and the lowest within the elderly. This pattern of infection is explained by a combination of the age-group contact structure and increasing immunity with age. We assess that SVs increased the overall attack rates by prolonging the pandemic into the winter. Vaccinating school children would have been the optimal strategy for minimizing infection rates in all age-groups. </AbstractText><CopyrightInformation>© 2016 The Author(s).</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Yaari</LastName><ForeName>R</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Bio-statistical Unit, The Gertner Institute for Epidemiology and Health Policy Research, Chaim Sheba Medical Center, Tel-Hashomer 52621, Israel Zoology Department, Tel-Aviv University, Ramat Aviv 69778, Israel.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Katriel</LastName><ForeName>G</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Department of Mathematics, ORT Braude College, Karmiel 21610, Israel.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Stone</LastName><ForeName>L</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Zoology Department, Tel-Aviv University, Ramat Aviv 69778, Israel School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Victoria 3001, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mendelson</LastName><ForeName>E</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Central Virology Laboratory, Ministry of Health, Chaim Sheba Medical Center, Tel-Hashomer 52621, Israel.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mandelboim</LastName><ForeName>M</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Central Virology Laboratory, Ministry of Health, Chaim Sheba Medical Center, Tel-Hashomer 52621, Israel.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Huppert</LastName><ForeName>A</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Bio-statistical Unit, The Gertner Institute for Epidemiology and Health Policy Research, Chaim Sheba Medical Center, Tel-Hashomer 52621, Israel Sackler Faculty of Medicine, Tel-Aviv University, Ramat Aviv 69778, Israel amith@gertner.health.gov.il.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>J R Soc Interface</MedlineTA><NlmUniqueID>101217269</NlmUniqueID><ISSNLinking>1742-5662</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000293" MajorTopicYN="N">Adolescent</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002648" MajorTopicYN="N">Child</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002675" MajorTopicYN="N">Child, Preschool</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016208" MajorTopicYN="Y">Databases, Factual</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007223" MajorTopicYN="N">Infant</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007231" MajorTopicYN="N">Infant, Newborn</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D053118" MajorTopicYN="Y">Influenza A Virus, H1N1 Subtype</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000517" MajorTopicYN="N">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008954" MajorTopicYN="Y">Models, Biological</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="Y">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014611" MajorTopicYN="N">Vaccination</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">absolute humidity</Keyword><Keyword MajorTopicYN="N">age structure</Keyword><Keyword MajorTopicYN="N">disease transmission model</Keyword><Keyword MajorTopicYN="N">school vacations</Keyword><Keyword MajorTopicYN="N">serology</Keyword><Keyword MajorTopicYN="N">vaccine 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