Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study
Identifieur interne : 004170 ( Main/Exploration ); précédent : 004169; suivant : 004171Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study
Auteurs : Phenyo E. Lekone [Royaume-Uni, Botswana] ; B Rbel F. Finkenst Dt [Royaume-Uni]Source :
- 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.
Url:
DOI: 10.1111/j.1541-0420.2006.00609.x
Affiliations:
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Lekone</name><affiliation wicri:level="1"><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Department of Statistics, University of Warwick, Coventry CV4 7AL</wicri:regionArea><wicri:noRegion>Coventry CV4 7AL</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Botswana</country></affiliation></author><author><name sortKey="Finkenst Dt, B Rbel F" sort="Finkenst Dt, B Rbel F" uniqKey="Finkenst Dt B" first="B Rbel F." last="Finkenst Dt">B Rbel F. Finkenst Dt</name><affiliation wicri:level="1"><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Department of Statistics, University of Warwick, Coventry CV4 7AL</wicri:regionArea><wicri:noRegion>Coventry CV4 7AL</wicri:noRegion></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. 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.</div></front></TEI><affiliations><list><country><li>Botswana</li><li>Royaume-Uni</li></country></list><tree><country name="Royaume-Uni"><noRegion><name sortKey="Lekone, Phenyo E" sort="Lekone, Phenyo E" uniqKey="Lekone P" first="Phenyo E." last="Lekone">Phenyo E. Lekone</name></noRegion><name sortKey="Finkenst Dt, B Rbel F" sort="Finkenst Dt, B Rbel F" uniqKey="Finkenst Dt B" first="B Rbel F." last="Finkenst Dt">B Rbel F. Finkenst Dt</name></country><country name="Botswana"><noRegion><name sortKey="Lekone, Phenyo E" sort="Lekone, Phenyo E" uniqKey="Lekone P" first="Phenyo E." last="Lekone">Phenyo E. Lekone</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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