Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study
Identifieur interne : 001D47 ( Istex/Corpus ); précédent : 001D46; suivant : 001D48Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study
Auteurs : Phenyo E. Lekone ; B Rbel F. Finkenst DtSource :
- Biometrics [ 0006-341X ] ; 2006-12.
Abstract
Summary A stochastic discrete‐time susceptible‐exposed‐infectious‐recovered (SEIR) model for infectious diseases is developed with the aim of estimating parameters from daily incidence and mortality time series for an outbreak of Ebola in the Democratic Republic of Congo in 1995. The incidence time series exhibit many low integers as well as zero counts requiring an intrinsically stochastic modeling approach. In order to capture the stochastic nature of the transitions between the compartmental populations in such a model we specify appropriate conditional binomial distributions. In addition, a relatively simple temporally varying transmission rate function is introduced that allows for the effect of control interventions. We develop Markov chain Monte Carlo methods for inference that are used to explore the posterior distribution of the parameters. The algorithm is further extended to integrate numerically over state variables of the model, which are unobserved. This provides a realistic stochastic model that can be used by epidemiologists to study the dynamics of the disease and the effect of control interventions.
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DOI: 10.1111/j.1541-0420.2006.00609.x
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Finkenst Dt</name><affiliation><mods:affiliation>Department of Statistics, University of Warwick, Coventry CV4 7AL, U.K.</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Biometrics</title><title level="j" type="alt">BIOMETRICS</title><idno type="ISSN">0006-341X</idno><idno type="eISSN">1541-0420</idno><imprint><biblScope unit="vol">62</biblScope><biblScope unit="issue">4</biblScope><biblScope unit="page" from="1170">1170</biblScope><biblScope unit="page" to="1177">1177</biblScope><biblScope unit="page-count">8</biblScope><publisher>Blackwell Publishing Inc</publisher><pubPlace>Malden, USA</pubPlace><date type="published" when="2006-12">2006-12</date></imprint><idno type="ISSN">0006-341X</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0006-341X</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Summary A stochastic discrete‐time susceptible‐exposed‐infectious‐recovered (SEIR) model for infectious diseases is developed with the aim of estimating parameters from daily incidence and mortality time series for an outbreak of Ebola in the Democratic Republic of Congo in 1995. The incidence time series exhibit many low integers as well as zero counts requiring an intrinsically stochastic modeling approach. In order to capture the stochastic nature of the transitions between the compartmental populations in such a model we specify appropriate conditional binomial distributions. In addition, a relatively simple temporally varying transmission rate function is introduced that allows for the effect of control interventions. We develop Markov chain Monte Carlo methods for inference that are used to explore the posterior distribution of the parameters. The algorithm is further extended to integrate numerically over state variables of the model, which are unobserved. 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The incidence time series exhibit many low integers as well as zero counts requiring an intrinsically stochastic modeling approach. In order to capture the stochastic nature of the transitions between the compartmental populations in such a model we specify appropriate conditional binomial distributions. In addition, a relatively simple temporally varying transmission rate function is introduced that allows for the effect of control interventions. We develop Markov chain Monte Carlo methods for inference that are used to explore the posterior distribution of the parameters. The algorithm is further extended to integrate numerically over state variables of the model, which are unobserved. This provides a realistic stochastic model that can be used by epidemiologists to study the dynamics of the disease and the effect of control interventions.</abstract><qualityIndicators><score>8.956</score><pdfWordCount>5916</pdfWordCount><pdfCharCount>34132</pdfCharCount><pdfVersion>1.3</pdfVersion><pdfPageCount>8</pdfPageCount><pdfPageSize>612 x 792 pts (letter)</pdfPageSize><pdfWordsPerPage>740</pdfWordsPerPage><pdfText>true</pdfText><refBibsNative>true</refBibsNative><abstractWordCount>163</abstractWordCount><abstractCharCount>1134</abstractCharCount><keywordCount>5</keywordCount></qualityIndicators><title>Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study</title><pmid><json:string>17156292</json:string></pmid><genre><json:string>article</json:string></genre><host><title>Biometrics</title><language><json:string>unknown</json:string></language><doi><json:string>10.1111/(ISSN)1541-0420</json:string></doi><issn><json:string>0006-341X</json:string></issn><eissn><json:string>1541-0420</json:string></eissn><publisherId><json:string>BIOM</json:string></publisherId><volume>62</volume><issue>4</issue><pages><first>1170</first><last>1177</last><total>8</total></pages><genre><json:string>journal</json:string></genre></host><namedEntities><unitex><date><json:string>2006</json:string><json:string>December 2006 1172</json:string><json:string>1995</json:string><json:string>December 2006 1174</json:string><json:string>from March 1 to July 16</json:string></date><geogName></geogName><orgName><json:string>ORS</json:string><json:string>Overseas Research Scheme</json:string><json:string>Model Diagnostics</json:string><json:string>The International</json:string><json:string>World Health Organization</json:string><json:string>Ebola-Sudan and the Ebola-Zaire</json:string></orgName><orgName_funder></orgName_funder><orgName_provider></orgName_provider><persName><json:string>Results</json:string><json:string>F. 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(2004)</json:string><json:string>Fearnhead 1170 and Meligkotsidou, 2004</json:string><json:string>Breman et al., 1977</json:string><json:string>Neal and Roberts, 2004</json:string><json:string>Neill and Roberts (1999)</json:string><json:string>Chib and Greenberg, 1994</json:string><json:string>O’Neill and Roberts, 1999</json:string><json:string>Biometrics, December 2006</json:string><json:string>Durham and Gallant, 2002</json:string><json:string>Mode and Sleeman (2000)</json:string><json:string>Gibson and Renshaw, 1998</json:string><json:string>Roberts and Rosenthal, 2001</json:string><json:string>Chowell et al., 2004</json:string><json:string>Andersson and Britton, 2000</json:string><json:string>Streftaris and Gibson, 2004</json:string><json:string>see Mode and Sleeman, 2000</json:string><json:string>Bailey, 1975</json:string><json:string>Hethcote, 2000</json:string><json:string>O’Neill, 2002</json:string><json:string>Becker, 1976</json:string><json:string>Elerian, Chib, and Shephard, 2001</json:string><json:string>Ball, Mollison, and Scalia-Tomba, 1997</json:string><json:string>see Anderson and May, 1991</json:string><json:string>Khan et al. 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<ref target="http://www.tei-c.org/">We use TEI</ref></desc></application></appInfo></encodingDesc><profileDesc><abstract xml:lang="en" style="main"><p><hi rend="bold"><hi rend="smallCaps">Summary</hi></hi> A stochastic discrete‐time susceptible‐exposed‐infectious‐recovered (SEIR) model for infectious diseases is developed with the aim of estimating parameters from daily incidence and mortality time series for an outbreak of Ebola in the Democratic Republic of Congo in 1995. The incidence time series exhibit many low integers as well as zero counts requiring an intrinsically stochastic modeling approach. In order to capture the stochastic nature of the transitions between the compartmental populations in such a model we specify appropriate conditional binomial distributions. In addition, a relatively simple temporally varying transmission rate function is introduced that allows for the effect of control interventions. We develop Markov chain Monte Carlo methods for inference that are used to explore the posterior distribution of the parameters. The algorithm is further extended to integrate numerically over state variables of the model, which are unobserved. This provides a realistic stochastic model that can be used by epidemiologists to study the dynamics of the disease and the effect of control interventions.</p></abstract><textClass><keywords xml:lang="en"><term xml:id="k1">Control intervention</term><term xml:id="k2">Ebola epidemics</term><term xml:id="k3">Estimating transition rates</term><term xml:id="k4">Latent process</term><term xml:id="k5">Stochastic SEIR model</term></keywords><keywords rend="tocHeading1"><term>REGULAR ARTICLES</term></keywords></textClass><langUsage><language ident="en"></language></langUsage></profileDesc><revisionDesc><change when="2019-12-20" who="#istex" xml:id="pub2tei">formatting</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/ark:/67375/WNG-69HPQM49-F/fulltext.txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component version="2.0" type="serialArticle" xml:lang="en"><header><publicationMeta level="product"><publisherInfo><publisherName>Blackwell Publishing Inc</publisherName><publisherLoc>Malden, USA</publisherLoc></publisherInfo><doi origin="wiley" registered="yes">10.1111/(ISSN)1541-0420</doi><issn type="print">0006-341X</issn><issn type="electronic">1541-0420</issn><idGroup><id type="product" value="BIOM"></id><id type="publisherDivision" value="ST"></id></idGroup><titleGroup><title type="main" sort="BIOMETRICS">Biometrics</title></titleGroup></publicationMeta><publicationMeta level="part" position="12104"><doi origin="wiley">10.1111/biom.2006.62.issue-4</doi><numberingGroup><numbering type="journalVolume" number="62">62</numbering><numbering type="journalIssue" number="4">4</numbering></numberingGroup><coverDate startDate="2006-12">December 2006</coverDate></publicationMeta><publicationMeta level="unit" type="article" position="24" status="forIssue"><doi origin="wiley">10.1111/j.1541-0420.2006.00609.x</doi><idGroup><id type="unit" value="BIOM609"></id></idGroup><countGroup><count type="pageTotal" number="8"></count></countGroup><titleGroup><title type="tocHeading1">REGULAR ARTICLES</title></titleGroup><eventGroup><event type="firstOnline" date="2006-06-02"></event><event type="publishedOnlineFinalForm" date="2006-06-02"></event><event type="xmlConverted" agent="Converter:BPG_TO_WML3G version:3.0.1 mode:FullText" date="2011-12-13"></event><event type="xmlConverted" agent="Converter:WILEY_ML3G_TO_WILEY_ML3GV2 version:3.8.8" date="2014-01-07"></event><event type="xmlConverted" agent="Converter:WML3G_To_WML3G version:4.1.7 mode:FullText,remove_FC" date="2014-10-15"></event></eventGroup><numberingGroup><numbering type="pageFirst" number="1170">1170</numbering><numbering type="pageLast" number="1177">1177</numbering></numberingGroup><correspondenceTo><i>email:</i><email>lekonepe@mopipi.ub.bw</email></correspondenceTo><linkGroup><link type="toTypesetVersion" href="file:BIOM.BIOM609.pdf"></link></linkGroup></publicationMeta><contentMeta><unparsedEditorialHistory><i>Received July</i> 2005. <i>Revised February</i> 2006. <i>Accepted March</i> 2006.</unparsedEditorialHistory><countGroup><count type="figureTotal" number="3"></count><count type="tableTotal" number="2"></count><count type="formulaTotal" number="32"></count><count type="referenceTotal" number="26"></count><count type="linksCrossRef" number="84"></count></countGroup><titleGroup><title type="main">Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study</title></titleGroup><creators><creator creatorRole="author" xml:id="cr1" affiliationRef="#a1" corresponding="yes"><personName><givenNames>Phenyo E.</givenNames><familyName>Lekone</familyName></personName></creator><creator creatorRole="author" xml:id="cr2" affiliationRef="#a1"><personName><givenNames>Bärbel F.</givenNames><familyName>Finkenstädt</familyName></personName></creator></creators><affiliationGroup><affiliation xml:id="a1"><unparsedAffiliation>Department of Statistics, University of Warwick, Coventry CV4 7AL, U.K.</unparsedAffiliation></affiliation></affiliationGroup><keywordGroup xml:lang="en"><keyword xml:id="k1">Control intervention</keyword><keyword xml:id="k2">Ebola epidemics</keyword><keyword xml:id="k3">Estimating transition rates</keyword><keyword xml:id="k4">Latent process</keyword><keyword xml:id="k5">Stochastic SEIR model</keyword></keywordGroup><abstractGroup><abstract type="main" xml:lang="en"><p><b><sc>Summary</sc></b> A stochastic discrete‐time susceptible‐exposed‐infectious‐recovered (SEIR) model for infectious diseases is developed with the aim of estimating parameters from daily incidence and mortality time series for an outbreak of Ebola in the Democratic Republic of Congo in 1995. The incidence time series exhibit many low integers as well as zero counts requiring an intrinsically stochastic modeling approach. In order to capture the stochastic nature of the transitions between the compartmental populations in such a model we specify appropriate conditional binomial distributions. In addition, a relatively simple temporally varying transmission rate function is introduced that allows for the effect of control interventions. We develop Markov chain Monte Carlo methods for inference that are used to explore the posterior distribution of the parameters. The algorithm is further extended to integrate numerically over state variables of the model, which are unobserved. This provides a realistic stochastic model that can be used by epidemiologists to study the dynamics of the disease and the effect of control interventions.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study</title></titleInfo><name type="personal"><namePart type="given">Phenyo E.</namePart><namePart type="family">Lekone</namePart><affiliation>Department of Statistics, University of Warwick, Coventry CV4 7AL, U.K.</affiliation><affiliation>E-mail: lekonepe@mopipi.ub.bw</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Bärbel F.</namePart><namePart type="family">Finkenstädt</namePart><affiliation>Department of Statistics, University of Warwick, Coventry CV4 7AL, U.K.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-6N5SZHKN-D">article</genre><originInfo><publisher>Blackwell Publishing Inc</publisher><place><placeTerm type="text">Malden, USA</placeTerm></place><dateIssued encoding="w3cdtf">2006-12</dateIssued><edition>Received July 2005. Revised February 2006. Accepted March 2006.</edition><copyrightDate encoding="w3cdtf">2006</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><extent unit="figures">3</extent><extent unit="tables">2</extent><extent unit="formulas">32</extent><extent unit="references">26</extent><extent unit="linksCrossRef">84</extent></physicalDescription><abstract lang="en">Summary A stochastic discrete‐time susceptible‐exposed‐infectious‐recovered (SEIR) model for infectious diseases is developed with the aim of estimating parameters from daily incidence and mortality time series for an outbreak of Ebola in the Democratic Republic of Congo in 1995. The incidence time series exhibit many low integers as well as zero counts requiring an intrinsically stochastic modeling approach. In order to capture the stochastic nature of the transitions between the compartmental populations in such a model we specify appropriate conditional binomial distributions. In addition, a relatively simple temporally varying transmission rate function is introduced that allows for the effect of control interventions. We develop Markov chain Monte Carlo methods for inference that are used to explore the posterior distribution of the parameters. The algorithm is further extended to integrate numerically over state variables of the model, which are unobserved. This provides a realistic stochastic model that can be used by epidemiologists to study the dynamics of the disease and the effect of control interventions.</abstract><subject lang="en"><genre>keywords</genre><topic>Control intervention</topic><topic>Ebola epidemics</topic><topic>Estimating transition rates</topic><topic>Latent process</topic><topic>Stochastic SEIR model</topic></subject><relatedItem type="host"><titleInfo><title>Biometrics</title></titleInfo><genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre><identifier type="ISSN">0006-341X</identifier><identifier type="eISSN">1541-0420</identifier><identifier type="DOI">10.1111/(ISSN)1541-0420</identifier><identifier type="PublisherID">BIOM</identifier><part><date>2006</date><detail type="volume"><caption>vol.</caption><number>62</number></detail><detail type="issue"><caption>no.</caption><number>4</number></detail><extent unit="pages"><start>1170</start><end>1177</end><total>8</total></extent></part></relatedItem><relatedItem type="references" displayLabel="cit1"><titleInfo><title>Anderson, R. 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