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Bayesian inference in an extended SEIR model with nonparametric disease transmission rate: an application to the Ebola epidemic in Sierra Leone
Frasso, Gianluca; Lambert, Philippe
2015
 

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Keywords :
Bayesian inference; Ebola 2014 epidemic; penalized B-splines; SEIR model; differential equations
Abstract :
[en] The 2014 Ebola outbreak in Sierra Leone is analyzed using an extension of the SEIR compartmental model. The unknown parameters of the system of differential equations are estimated by combining data on the number of new (laboratory confirmed) Ebola cases reported by the Ministry of Health and prior distributions for the transition rates elicited using information collected by the WHO Response Team (2014) during the follow-up of specific Ebola cases. The evolution over time of the disease transmission rate is modeled nonparametrically using penalized B-splines. Our framework represents a valuable and robust stochastic tool for the study of an epidemic dynamic from irregular and possibly aggregated case data. Simulations and the analysis of the 2014 Sierra Leone Ebola data highlight the merits of the proposed methodology.
Disciplines :
Mathematics
Author, co-author :
Frasso, Gianluca ;  Université de Liège > Institut des sciences humaines et sociales > Méthodes quantitatives en sciences sociales
Lambert, Philippe  ;  Université de Liège > Institut des sciences humaines et sociales > Méthodes quantitatives en sciences sociales
Language :
English
Title :
Bayesian inference in an extended SEIR model with nonparametric disease transmission rate: an application to the Ebola epidemic in Sierra Leone
Publication date :
29 May 2015
Number of pages :
25
Name of the research project :
IAP research network P7/06 (StUDyS)
Funders :
Belgian Government (Belgian Science Policy)
Projet d'Actions de Recherche Concertées (ARC) 11/16-039 de la Communauté française de Belgique
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since 29 May 2015

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