Joint modelling of serological and hospitalization data reveals that high levels of pre-existing immunity and school holidays shaped the influenza A pandemic of 2009 in the Netherlands.
Identifieur interne : 000092 ( Main/Exploration ); précédent : 000091; suivant : 000093Joint modelling of serological and hospitalization data reveals that high levels of pre-existing immunity and school holidays shaped the influenza A pandemic of 2009 in the Netherlands.
Auteurs : Dennis E. Te Beest [Pays-Bas] ; Paul J. Birrell ; Jacco Wallinga [Pays-Bas] ; Daniela De Angelis ; Michiel Van Boven [Pays-Bas]Source :
- Journal of the Royal Society, Interface [ 1742-5662 ] ; 2015.
Descripteurs français
- KwdFr :
- Adolescent (MeSH), Adulte (MeSH), Adulte d'âge moyen (MeSH), Enfant (MeSH), Enfant d'âge préscolaire (MeSH), Facteurs âges (MeSH), Femelle (MeSH), Grippe humaine (immunologie), Grippe humaine (transmission), Grippe humaine (épidémiologie), Hospitalisation (MeSH), Humains (MeSH), Modèles biologiques (MeSH), Mâle (MeSH), Pandémies (MeSH), Pays-Bas (épidémiologie), Sujet âgé (MeSH), Études transversales (MeSH).
- MESH :
- immunologie : Grippe humaine.
- épidémiologie : Grippe humaine, Pays-Bas.
- Adolescent, Adulte, Adulte d'âge moyen, Enfant, Enfant d'âge préscolaire, Facteurs âges, Femelle, Hospitalisation, Humains, Modèles biologiques, Mâle, Pandémies, Sujet âgé, Études transversales.
- Wicri :
- geographic : Pays-Bas.
English descriptors
- KwdEn :
- Adolescent (MeSH), Adult (MeSH), Age Factors (MeSH), Aged (MeSH), Child (MeSH), Child, Preschool (MeSH), Cross-Sectional Studies (MeSH), Female (MeSH), Hospitalization (MeSH), Humans (MeSH), Influenza, Human (epidemiology), Influenza, Human (immunology), Influenza, Human (transmission), Male (MeSH), Middle Aged (MeSH), Models, Biological (MeSH), Netherlands (epidemiology), Pandemics (MeSH).
- MESH :
- geographic , epidemiology : Netherlands.
- epidemiology : Influenza, Human.
- immunology : Influenza, Human.
- transmission : Influenza, Human.
- Adolescent, Adult, Age Factors, Aged, Child, Child, Preschool, Cross-Sectional Studies, Female, Hospitalization, Humans, Male, Middle Aged, Models, Biological, Pandemics.
Abstract
Obtaining a quantitative understanding of the transmission dynamics of influenza A is important for predicting healthcare demand and assessing the likely impact of intervention measures. The pandemic of 2009 provides an ideal platform for developing integrative analyses as it has been studied intensively, and a wealth of data sources is available. Here, we analyse two complementary datasets in a disease transmission framework: cross-sectional serological surveys providing data on infection attack rates, and hospitalization data that convey information on the timing and duration of the pandemic. We estimate key epidemic determinants such as infection and hospitalization rates, and the impact of a school holiday. In contrast to previous approaches, our novel modelling of serological data with mixture distributions provides a probabilistic classification of individual samples (susceptible, immune and infected), propagating classification uncertainties to the transmission model and enabling serological classifications to be informed by hospitalization data. The analyses show that high levels of immunity among persons 20 years and older provide a consistent explanation of the skewed attack rates observed during the pandemic and yield precise estimates of the probability of hospitalization per infection (1-4 years: 0.00096 (95%CrI: 0.00078-0.0012); 5-19 years: 0.00036 (0.00031-0.0044); 20-64 years: 0.0015 (0.00091-0.0020); 65+ years: 0.0084 (0.0028-0.016)). The analyses suggest that in The Netherlands, the school holiday period reduced the number of infectious contacts between 5- and 9-year-old children substantially (estimated reduction: 54%; 95%CrI: 29-82%), thereby delaying the unfolding of the pandemic in The Netherlands by approximately a week.
DOI: 10.1098/rsif.2014.1244
PubMed: 25540241
PubMed Central: PMC4305427
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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(MeSH)</term><term>Femelle (MeSH)</term><term>Grippe humaine (immunologie)</term><term>Grippe humaine (transmission)</term><term>Grippe humaine (épidémiologie)</term><term>Hospitalisation (MeSH)</term><term>Humains (MeSH)</term><term>Modèles biologiques (MeSH)</term><term>Mâle (MeSH)</term><term>Pandémies (MeSH)</term><term>Pays-Bas (épidémiologie)</term><term>Sujet âgé (MeSH)</term><term>Études transversales (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Netherlands</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="immunologie" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="immunology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" 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xml:lang="fr"><term>Pays-Bas</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Obtaining a quantitative understanding of the transmission dynamics of influenza A is important for predicting healthcare demand and assessing the likely impact of intervention measures. The pandemic of 2009 provides an ideal platform for developing integrative analyses as it has been studied intensively, and a wealth of data sources is available. Here, we analyse two complementary datasets in a disease transmission framework: cross-sectional serological surveys providing data on infection attack rates, and hospitalization data that convey information on the timing and duration of the pandemic. We estimate key epidemic determinants such as infection and hospitalization rates, and the impact of a school holiday. In contrast to previous approaches, our novel modelling of serological data with mixture distributions provides a probabilistic classification of individual samples (susceptible, immune and infected), propagating classification uncertainties to the transmission model and enabling serological classifications to be informed by hospitalization data. The analyses show that high levels of immunity among persons 20 years and older provide a consistent explanation of the skewed attack rates observed during the pandemic and yield precise estimates of the probability of hospitalization per infection (1-4 years: 0.00096 (95%CrI: 0.00078-0.0012); 5-19 years: 0.00036 (0.00031-0.0044); 20-64 years: 0.0015 (0.00091-0.0020); 65+ years: 0.0084 (0.0028-0.016)). The analyses suggest that in The Netherlands, the school holiday period reduced the number of infectious contacts between 5- and 9-year-old children substantially (estimated reduction: 54%; 95%CrI: 29-82%), thereby delaying the unfolding of the pandemic in The Netherlands by approximately a week. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25540241</PMID><DateCompleted><Year>2015</Year><Month>08</Month><Day>12</Day></DateCompleted><DateRevised><Year>2020</Year><Month>03</Month><Day>06</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1742-5662</ISSN><JournalIssue CitedMedium="Internet"><Volume>12</Volume><Issue>103</Issue><PubDate><Year>2015</Year><Month>Feb</Month><Day>06</Day></PubDate></JournalIssue><Title>Journal of the Royal Society, Interface</Title><ISOAbbreviation>J R Soc Interface</ISOAbbreviation></Journal><ArticleTitle>Joint modelling of serological and hospitalization data reveals that high levels of pre-existing immunity and school holidays shaped the influenza A pandemic of 2009 in the Netherlands.</ArticleTitle><ELocationID EIdType="doi" ValidYN="Y">10.1098/rsif.2014.1244</ELocationID><ELocationID EIdType="pii" ValidYN="Y">20141244</ELocationID><Abstract><AbstractText>Obtaining a quantitative understanding of the transmission dynamics of influenza A is important for predicting healthcare demand and assessing the likely impact of intervention measures. The pandemic of 2009 provides an ideal platform for developing integrative analyses as it has been studied intensively, and a wealth of data sources is available. Here, we analyse two complementary datasets in a disease transmission framework: cross-sectional serological surveys providing data on infection attack rates, and hospitalization data that convey information on the timing and duration of the pandemic. We estimate key epidemic determinants such as infection and hospitalization rates, and the impact of a school holiday. In contrast to previous approaches, our novel modelling of serological data with mixture distributions provides a probabilistic classification of individual samples (susceptible, immune and infected), propagating classification uncertainties to the transmission model and enabling serological classifications to be informed by hospitalization data. The analyses show that high levels of immunity among persons 20 years and older provide a consistent explanation of the skewed attack rates observed during the pandemic and yield precise estimates of the probability of hospitalization per infection (1-4 years: 0.00096 (95%CrI: 0.00078-0.0012); 5-19 years: 0.00036 (0.00031-0.0044); 20-64 years: 0.0015 (0.00091-0.0020); 65+ years: 0.0084 (0.0028-0.016)). The analyses suggest that in The Netherlands, the school holiday period reduced the number of infectious contacts between 5- and 9-year-old children substantially (estimated reduction: 54%; 95%CrI: 29-82%), thereby delaying the unfolding of the pandemic in The Netherlands by approximately a week. </AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Te Beest</LastName><ForeName>Dennis E</ForeName><Initials>DE</Initials><AffiliationInfo><Affiliation>Centre for Infectious Disease Control, National Institute for Public Health and the Environment, PO Box 1, Bilthoven 3720AB, The Netherlands.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Birrell</LastName><ForeName>Paul J</ForeName><Initials>PJ</Initials><AffiliationInfo><Affiliation>MRC Biostatistics Unit, Cambridge Institute of Public Health, CB2 0SR.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wallinga</LastName><ForeName>Jacco</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Centre for Infectious Disease Control, National Institute for Public Health and the Environment, PO Box 1, Bilthoven 3720AB, The Netherlands.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>De Angelis</LastName><ForeName>Daniela</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>MRC Biostatistics Unit, Cambridge Institute of Public Health, CB2 0SR.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>van Boven</LastName><ForeName>Michiel</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Centre for Infectious Disease Control, National Institute for Public Health and the Environment, PO Box 1, Bilthoven 3720AB, The Netherlands michiel.van.boven@rivm.nl.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>11/46/03</GrantID><Acronym>DH_</Acronym><Agency>Department of Health</Agency><Country>United Kingdom</Country></Grant><Grant><GrantID>MC_U105260556</GrantID><Acronym>MRC_</Acronym><Agency>Medical Research Council</Agency><Country>United Kingdom</Country></Grant><Grant><GrantID>U105260566</GrantID><Acronym>MRC_</Acronym><Agency>Medical Research Council</Agency><Country>United Kingdom</Country></Grant></GrantList><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="D000367" MajorTopicYN="N">Age Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002648" MajorTopicYN="N">Child</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002675" MajorTopicYN="N">Child, Preschool</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003430" MajorTopicYN="N">Cross-Sectional Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006760" MajorTopicYN="Y">Hospitalization</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="Y">Influenza, Human</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName><QualifierName UI="Q000635" MajorTopicYN="N">transmission</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="D009426" MajorTopicYN="N" Type="Geographic">Netherlands</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="Y">Pandemics</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Bayesian evidence synthesis</Keyword><Keyword MajorTopicYN="N">hospitalization incidence</Keyword><Keyword MajorTopicYN="N">influenza A</Keyword><Keyword MajorTopicYN="N">mixture analysis</Keyword><Keyword MajorTopicYN="N">serology</Keyword><Keyword MajorTopicYN="N">transmission model</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>12</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>12</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>8</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25540241</ArticleId><ArticleId IdType="pii">rsif.2014.1244</ArticleId><ArticleId IdType="doi">10.1098/rsif.2014.1244</ArticleId><ArticleId 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J" sort="Birrell, Paul J" uniqKey="Birrell P" first="Paul J" last="Birrell">Paul J. Birrell</name><name sortKey="De Angelis, Daniela" sort="De Angelis, Daniela" uniqKey="De Angelis D" first="Daniela" last="De Angelis">Daniela De Angelis</name></noCountry><country name="Pays-Bas"><noRegion><name sortKey="Te Beest, Dennis E" sort="Te Beest, Dennis E" uniqKey="Te Beest D" first="Dennis E" last="Te Beest">Dennis E. Te Beest</name></noRegion><name sortKey="Van Boven, Michiel" sort="Van Boven, Michiel" uniqKey="Van Boven M" first="Michiel" last="Van Boven">Michiel Van Boven</name><name sortKey="Wallinga, Jacco" sort="Wallinga, Jacco" uniqKey="Wallinga J" first="Jacco" last="Wallinga">Jacco Wallinga</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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