Predictive assessment of a non-linear random effects model for multivariate time series of infectious disease counts.
Identifieur interne : 000283 ( Main/Exploration ); précédent : 000282; suivant : 000284Predictive assessment of a non-linear random effects model for multivariate time series of infectious disease counts.
Auteurs : M. Paul [Suisse] ; L. HeldSource :
- Statistics in medicine [ 1097-0258 ] ; 2011.
Descripteurs français
- KwdFr :
- MESH :
- Wicri :
English descriptors
- KwdEn :
- MESH :
- geographic : France, Germany.
- epidemiology : Communicable Diseases, Influenza, Human, Meningococcal Infections.
- methods : Population Surveillance.
- Disease Outbreaks, Humans, Incidence, Nonlinear Dynamics, Numerical Analysis, Computer-Assisted.
Abstract
Infectious disease counts from surveillance systems are typically observed in several administrative geographical areas. In this paper, a non-linear model for the analysis of such multiple time series of counts is discussed. To account for heterogeneous incidence levels or varying transmission of a pathogen across regions, region-specific and possibly spatially correlated random effects are introduced. Inference is based on penalized likelihood methodology for mixed models. Since the use of classical model choice criteria such as AIC or BIC can be problematic in the presence of random effects, models are compared by means of one-step-ahead predictions and proper scoring rules. In a case study, the model is applied to monthly counts of meningococcal disease cases in 94 departments of France (excluding Corsica) and weekly counts of influenza cases in 140 administrative districts of Southern Germany. The predictive performance improves if existing heterogeneity is accounted for by random effects.
DOI: 10.1002/sim.4177
PubMed: 21484849
Affiliations:
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Paul</name><affiliation wicri:level="4"><nlm:affiliation>Biostatistics Unit, Institute of Social and Preventive Medicine, University of Zurich, Switzerland. michaela.paul@ifspm.uzh.ch</nlm:affiliation><country xml:lang="fr">Suisse</country><wicri:regionArea>Biostatistics Unit, Institute of Social and Preventive Medicine, University of Zurich</wicri:regionArea><placeName><settlement type="city">Zurich</settlement><region nuts="3" type="region">Canton de Zurich</region></placeName><orgName type="university">Université de Zurich</orgName></affiliation></author><author><name sortKey="Held, L" sort="Held, L" uniqKey="Held L" first="L" last="Held">L. Held</name></author></analytic><series><title level="j">Statistics in medicine</title><idno type="eISSN">1097-0258</idno><imprint><date when="2011" type="published">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Communicable Diseases (epidemiology)</term><term>Disease Outbreaks</term><term>France</term><term>Germany</term><term>Humans</term><term>Incidence</term><term>Influenza, Human (epidemiology)</term><term>Meningococcal Infections (epidemiology)</term><term>Nonlinear Dynamics</term><term>Numerical Analysis, Computer-Assisted</term><term>Population Surveillance (methods)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Allemagne</term><term>Analyse numérique assistée par ordinateur</term><term>Dynamique non linéaire</term><term>Flambées de maladies</term><term>France</term><term>Grippe humaine (épidémiologie)</term><term>Humains</term><term>Incidence</term><term>Infections à méningocoques (épidémiologie)</term><term>Maladies transmissibles (épidémiologie)</term><term>Surveillance de la population ()</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>France</term><term>Germany</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Communicable Diseases</term><term>Influenza, Human</term><term>Meningococcal Infections</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Population Surveillance</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term><term>Infections à méningocoques</term><term>Maladies transmissibles</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Disease Outbreaks</term><term>Humans</term><term>Incidence</term><term>Nonlinear Dynamics</term><term>Numerical Analysis, Computer-Assisted</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Allemagne</term><term>Analyse numérique assistée par ordinateur</term><term>Dynamique non linéaire</term><term>Flambées de maladies</term><term>France</term><term>Humains</term><term>Incidence</term><term>Surveillance de la population</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>France</term><term>Allemagne</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Infectious disease counts from surveillance systems are typically observed in several administrative geographical areas. In this paper, a non-linear model for the analysis of such multiple time series of counts is discussed. To account for heterogeneous incidence levels or varying transmission of a pathogen across regions, region-specific and possibly spatially correlated random effects are introduced. Inference is based on penalized likelihood methodology for mixed models. Since the use of classical model choice criteria such as AIC or BIC can be problematic in the presence of random effects, models are compared by means of one-step-ahead predictions and proper scoring rules. In a case study, the model is applied to monthly counts of meningococcal disease cases in 94 departments of France (excluding Corsica) and weekly counts of influenza cases in 140 administrative districts of Southern Germany. The predictive performance improves if existing heterogeneity is accounted for by random effects.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">21484849</PMID><DateCompleted><Year>2011</Year><Month>07</Month><Day>21</Day></DateCompleted><DateRevised><Year>2011</Year><Month>04</Month><Day>12</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1097-0258</ISSN><JournalIssue CitedMedium="Internet"><Volume>30</Volume><Issue>10</Issue><PubDate><Year>2011</Year><Month>May</Month><Day>10</Day></PubDate></JournalIssue><Title>Statistics in medicine</Title><ISOAbbreviation>Stat Med</ISOAbbreviation></Journal><ArticleTitle>Predictive assessment of a non-linear random effects model for multivariate time series of infectious disease counts.</ArticleTitle><Pagination><MedlinePgn>1118-36</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1002/sim.4177</ELocationID><Abstract><AbstractText>Infectious disease counts from surveillance systems are typically observed in several administrative geographical areas. In this paper, a non-linear model for the analysis of such multiple time series of counts is discussed. To account for heterogeneous incidence levels or varying transmission of a pathogen across regions, region-specific and possibly spatially correlated random effects are introduced. Inference is based on penalized likelihood methodology for mixed models. Since the use of classical model choice criteria such as AIC or BIC can be problematic in the presence of random effects, models are compared by means of one-step-ahead predictions and proper scoring rules. In a case study, the model is applied to monthly counts of meningococcal disease cases in 94 departments of France (excluding Corsica) and weekly counts of influenza cases in 140 administrative districts of Southern Germany. The predictive performance improves if existing heterogeneity is accounted for by random effects.</AbstractText><CopyrightInformation>Copyright © 2011 John Wiley & Sons, Ltd.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Paul</LastName><ForeName>M</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Biostatistics Unit, Institute of Social and Preventive Medicine, University of Zurich, Switzerland. michaela.paul@ifspm.uzh.ch</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Held</LastName><ForeName>L</ForeName><Initials>L</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D003160">Comparative Study</PublicationType><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>01</Month><Day>17</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Stat Med</MedlineTA><NlmUniqueID>8215016</NlmUniqueID><ISSNLinking>0277-6715</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D003141" MajorTopicYN="N">Communicable Diseases</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004196" MajorTopicYN="Y">Disease Outbreaks</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005602" MajorTopicYN="N" Type="Geographic">France</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005858" MajorTopicYN="N" Type="Geographic">Germany</DescriptorName></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="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008589" MajorTopicYN="N">Meningococcal Infections</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017711" MajorTopicYN="Y">Nonlinear Dynamics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009716" MajorTopicYN="N">Numerical Analysis, Computer-Assisted</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011159" MajorTopicYN="N">Population Surveillance</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2009</Year><Month>09</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2010</Year><Month>12</Month><Day>02</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2011</Year><Month>4</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2011</Year><Month>4</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2011</Year><Month>7</Month><Day>22</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">21484849</ArticleId><ArticleId IdType="doi">10.1002/sim.4177</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Suisse</li></country><region><li>Canton de Zurich</li></region><settlement><li>Zurich</li></settlement><orgName><li>Université de Zurich</li></orgName></list><tree><noCountry><name sortKey="Held, L" sort="Held, L" uniqKey="Held L" first="L" last="Held">L. Held</name></noCountry><country name="Suisse"><region name="Canton de Zurich"><name sortKey="Paul, M" sort="Paul, M" uniqKey="Paul M" first="M" last="Paul">M. Paul</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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