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Inference in a stochastic SEIR model using Sequential Monte Carlo methods
Bonou, Wilfried; LAMBERT, Philippe
2016In The 37th Annual Conference of the International Society for Clinical Biostatistics (ISCB): Book of Abstracts. Birmingham, UK, 21-25 August 2016
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Keywords :
Stochastic Dynamic Epidemic Model; State Space Models; Sequential Monte Carlo
Abstract :
[en] Many biological, physical, chemical, economic, and social phenomena are dynamic and are modeled using (systems of) Ordinary Differential Equations (ODEs). But a more realistic way to describe these dynamics relies on Discrete Time Markov Chains (DTMC). There is a growing interest in the development of Bayesian statistical methods to infer on the parameters in such dynamic models, particularly those defining epidemic spread, by combining prior information with experimental or observational data. Our proposal aims to explore the merits of the Bayesian Optimal Filtering technique in the estimation of the parameters of a stochastic SEIR (S = Susceptible, E = Exposed, I = Infectious, R = Removed) epidemic model. State Space Models (SSMs) are used to describe the epidemic dynamic. The unknown static parameters are estimated using a combination of Sequential Monte Carlo techniques with a Markov Chain Monte Carlo algorithm, . . .
Disciplines :
Mathematics
Author, co-author :
Bonou, Wilfried ;  Université de Liège > Faculté des sciences sociales > Méthodes quantitatives en sciences sociales
LAMBERT, Philippe;  Université de Liège - ULiège > Faculté des Sciences Sociales > Méthodes Quantitatives en Sciences Sociales
Language :
English
Title :
Inference in a stochastic SEIR model using Sequential Monte Carlo methods
Publication date :
August 2016
Event name :
The 37th Annual Conference of the International Society for Clinical Biostatistics (ISCB)
Event place :
Birmingham, United Kingdom
Event date :
21-25 August 2016.
Audience :
International
Main work title :
The 37th Annual Conference of the International Society for Clinical Biostatistics (ISCB): Book of Abstracts. Birmingham, UK, 21-25 August 2016
Peer reviewed :
Peer reviewed
Available on ORBi :
since 28 September 2016

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