Bayesian hierarchical Poisson models with a hidden Markov structure for the detection of influenza epidemic outbreaks.
Identifieur interne : 000191 ( Main/Exploration ); précédent : 000190; suivant : 000192Bayesian hierarchical Poisson models with a hidden Markov structure for the detection of influenza epidemic outbreaks.
Auteurs : D. Conesa [Espagne] ; M A Martínez-Beneito [Espagne] ; R. Amor S [Espagne] ; A. L Pez-Quílez [Espagne]Source :
- Statistical methods in medical research [ 1477-0334 ] ; 2015.
Descripteurs français
- KwdFr :
- Biostatistiques (MeSH), Chaines de Markov (MeSH), Espagne (épidémiologie), Grippe humaine (épidémiologie), Humains (MeSH), Incidence (MeSH), Internet (MeSH), Loi de Poisson (MeSH), Modèles statistiques (MeSH), Moteur de recherche (MeSH), Méthode de Monte Carlo (MeSH), Probabilité (MeSH), Surveillance sentinelle (MeSH), Théorème de Bayes (MeSH), Épidémies (statistiques et données numériques), Épidémies de maladies (MeSH).
- MESH :
- statistiques et données numériques : Épidémies.
- épidémiologie : Espagne, Grippe humaine.
- Biostatistiques, Chaines de Markov, Humains, Incidence, Internet, Loi de Poisson, Modèles statistiques, Moteur de recherche, Méthode de Monte Carlo, Probabilité, Surveillance sentinelle, Théorème de Bayes, Épidémies de maladies.
- Wicri :
- geographic : Espagne.
English descriptors
- KwdEn :
- Bayes Theorem (MeSH), Biostatistics (MeSH), Disease Outbreaks (MeSH), Epidemics (statistics & numerical data), Humans (MeSH), Incidence (MeSH), Influenza, Human (epidemiology), Internet (MeSH), Markov Chains (MeSH), Models, Statistical (MeSH), Monte Carlo Method (MeSH), Poisson Distribution (MeSH), Probability (MeSH), Search Engine (MeSH), Sentinel Surveillance (MeSH), Spain (epidemiology).
- MESH :
- geographic , epidemiology : Spain.
- epidemiology : Influenza, Human.
- statistics & numerical data : Epidemics.
- Bayes Theorem, Biostatistics, Disease Outbreaks, Humans, Incidence, Internet, Markov Chains, Models, Statistical, Monte Carlo Method, Poisson Distribution, Probability, Search Engine, Sentinel Surveillance.
Abstract
Considerable effort has been devoted to the development of statistical algorithms for the automated monitoring of influenza surveillance data. In this article, we introduce a framework of models for the early detection of the onset of an influenza epidemic which is applicable to different kinds of surveillance data. In particular, the process of the observed cases is modelled via a Bayesian Hierarchical Poisson model in which the intensity parameter is a function of the incidence rate. The key point is to consider this incidence rate as a normal distribution in which both parameters (mean and variance) are modelled differently, depending on whether the system is in an epidemic or non-epidemic phase. To do so, we propose a hidden Markov model in which the transition between both phases is modelled as a function of the epidemic state of the previous week. Different options for modelling the rates are described, including the option of modelling the mean at each phase as autoregressive processes of order 0, 1 or 2. Bayesian inference is carried out to provide the probability of being in an epidemic state at any given moment. The methodology is applied to various influenza data sets. The results indicate that our methods outperform previous approaches in terms of sensitivity, specificity and timeliness.
DOI: 10.1177/0962280211414853
PubMed: 21873301
Affiliations:
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Cataluña 21, 46021 Valencia, Spain.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Centro Superior de Investigación en Salud Pública, Generalitat Valenciana, Av. Cataluña 21, 46021 Valencia</wicri:regionArea><placeName><region nuts="2" type="communauté">Communauté valencienne</region></placeName></affiliation></author><author><name sortKey="Amor S, R" sort="Amor S, R" uniqKey="Amor S R" first="R" last="Amor S">R. Amor S</name><affiliation wicri:level="2"><nlm:affiliation>Centro Superior de Investigación en Salud Pública, Generalitat Valenciana, Av. Cataluña 21, 46021 Valencia, Spain. Departamento de Ciencias Físicas, Matemáticas y Computación, Universidad Cardenal Herrera-CEU, Ed. Seminario, 46113 Moncada (Valencia), Spain.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Centro Superior de Investigación en Salud Pública, Generalitat Valenciana, Av. Cataluña 21, 46021 Valencia, Spain. Departamento de Ciencias Físicas, Matemáticas y Computación, Universidad Cardenal Herrera-CEU, Ed. Seminario, 46113 Moncada (Valencia)</wicri:regionArea><placeName><region nuts="2" type="communauté">Communauté valencienne</region></placeName></affiliation></author><author><name sortKey="L Pez Quilez, A" sort="L Pez Quilez, A" uniqKey="L Pez Quilez A" first="A" last="L Pez-Quílez">A. 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In this article, we introduce a framework of models for the early detection of the onset of an influenza epidemic which is applicable to different kinds of surveillance data. In particular, the process of the observed cases is modelled via a Bayesian Hierarchical Poisson model in which the intensity parameter is a function of the incidence rate. The key point is to consider this incidence rate as a normal distribution in which both parameters (mean and variance) are modelled differently, depending on whether the system is in an epidemic or non-epidemic phase. To do so, we propose a hidden Markov model in which the transition between both phases is modelled as a function of the epidemic state of the previous week. Different options for modelling the rates are described, including the option of modelling the mean at each phase as autoregressive processes of order 0, 1 or 2. Bayesian inference is carried out to provide the probability of being in an epidemic state at any given moment. The methodology is applied to various influenza data sets. The results indicate that our methods outperform previous approaches in terms of sensitivity, specificity and timeliness. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">21873301</PMID><DateCompleted><Year>2016</Year><Month>04</Month><Day>22</Day></DateCompleted><DateRevised><Year>2015</Year><Month>04</Month><Day>17</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1477-0334</ISSN><JournalIssue CitedMedium="Internet"><Volume>24</Volume><Issue>2</Issue><PubDate><Year>2015</Year><Month>Apr</Month></PubDate></JournalIssue><Title>Statistical methods in medical research</Title><ISOAbbreviation>Stat Methods Med Res</ISOAbbreviation></Journal><ArticleTitle>Bayesian hierarchical Poisson models with a hidden Markov structure for the detection of influenza epidemic outbreaks.</ArticleTitle><Pagination><MedlinePgn>206-23</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1177/0962280211414853</ELocationID><Abstract><AbstractText>Considerable effort has been devoted to the development of statistical algorithms for the automated monitoring of influenza surveillance data. In this article, we introduce a framework of models for the early detection of the onset of an influenza epidemic which is applicable to different kinds of surveillance data. In particular, the process of the observed cases is modelled via a Bayesian Hierarchical Poisson model in which the intensity parameter is a function of the incidence rate. The key point is to consider this incidence rate as a normal distribution in which both parameters (mean and variance) are modelled differently, depending on whether the system is in an epidemic or non-epidemic phase. To do so, we propose a hidden Markov model in which the transition between both phases is modelled as a function of the epidemic state of the previous week. Different options for modelling the rates are described, including the option of modelling the mean at each phase as autoregressive processes of order 0, 1 or 2. Bayesian inference is carried out to provide the probability of being in an epidemic state at any given moment. The methodology is applied to various influenza data sets. The results indicate that our methods outperform previous approaches in terms of sensitivity, specificity and timeliness. </AbstractText><CopyrightInformation>© The Author(s) 2011 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Conesa</LastName><ForeName>D</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Departament d'Estadística i Investigació Operativa, Universitat de València, C/ Dr. Moliner 50, 46100 Burjassot (Valencia), Spain. david.v.conesa@uv.es.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Martínez-Beneito</LastName><ForeName>M A</ForeName><Initials>MA</Initials><AffiliationInfo><Affiliation>Centro Superior de Investigación en Salud Pública, Generalitat Valenciana, Av. Cataluña 21, 46021 Valencia, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Amorós</LastName><ForeName>R</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Centro Superior de Investigación en Salud Pública, Generalitat Valenciana, Av. Cataluña 21, 46021 Valencia, Spain. Departamento de Ciencias Físicas, Matemáticas y Computación, Universidad Cardenal Herrera-CEU, Ed. Seminario, 46113 Moncada (Valencia), Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>López-Quílez</LastName><ForeName>A</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Departament d'Estadística i Investigació Operativa, Universitat de València, C/ Dr. Moliner 50, 46100 Burjassot (Valencia), Spain.</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><ArticleDate DateType="Electronic"><Year>2011</Year><Month>08</Month><Day>25</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Stat Methods Med Res</MedlineTA><NlmUniqueID>9212457</NlmUniqueID><ISSNLinking>0962-2802</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D001499" MajorTopicYN="N">Bayes Theorem</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D056808" MajorTopicYN="N">Biostatistics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004196" MajorTopicYN="N">Disease Outbreaks</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058872" MajorTopicYN="Y">Epidemics</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015994" MajorTopicYN="N">Incidence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D020407" MajorTopicYN="N">Internet</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008390" MajorTopicYN="N">Markov Chains</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="Y">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009010" MajorTopicYN="N">Monte Carlo Method</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016012" MajorTopicYN="N">Poisson Distribution</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011336" MajorTopicYN="N">Probability</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D057386" MajorTopicYN="N">Search Engine</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018571" MajorTopicYN="N">Sentinel Surveillance</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013030" MajorTopicYN="N" Type="Geographic">Spain</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Bayesian inference</Keyword><Keyword MajorTopicYN="N">autoregressive modelling</Keyword><Keyword MajorTopicYN="N">hidden Markov models</Keyword><Keyword MajorTopicYN="N">influenza</Keyword><Keyword MajorTopicYN="N">public health</Keyword><Keyword MajorTopicYN="N">temporal surveillance</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2011</Year><Month>8</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2011</Year><Month>8</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2016</Year><Month>4</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">21873301</ArticleId><ArticleId IdType="pii">0962280211414853</ArticleId><ArticleId IdType="doi">10.1177/0962280211414853</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Espagne</li></country><region><li>Communauté valencienne</li></region></list><tree><country name="Espagne"><region name="Communauté valencienne"><name sortKey="Conesa, D" sort="Conesa, D" uniqKey="Conesa D" first="D" last="Conesa">D. Conesa</name></region><name sortKey="Amor S, R" sort="Amor S, R" uniqKey="Amor S R" first="R" last="Amor S">R. Amor S</name><name sortKey="L Pez Quilez, A" sort="L Pez Quilez, A" uniqKey="L Pez Quilez A" first="A" last="L Pez-Quílez">A. L Pez-Quílez</name><name sortKey="Martinez Beneito, M A" sort="Martinez Beneito, M A" uniqKey="Martinez Beneito M" first="M A" last="Martínez-Beneito">M A Martínez-Beneito</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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