Stochastic SIR Epidemic Model; State Space Models; Sequential Monte Carlo; Particle Marginal Metropolis-Hastings.
Abstract :
[en] We consider State Space Models (SSMs) as Discrete Time Markov
Chains (DTMC) to describe a stochastic SIR Epidemic dynamic. The unknown
static parameters are estimated by combining Sequential Monte Carlo and Markov
Chain Monte Carlo algorithms (SMC-within-MCMC) also known as Particle
Marginal Metropolis-Hastings (PMMH). The performances of the strategy are
evaluated using simulations. The method is illustrated by modeling the spread of
a viral infection in a small community.
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 SIR epidemic model using Bayesian filtering, Rennes, France, 4-8 July, 2016, pp.41-46.
Publication date :
July 2016
Event name :
The 31st International Workshop on Statistical Modelling
Event place :
Rennes, France
Event date :
4-8 July, 2016
By request :
Yes
Audience :
International
Main work title :
Proceedings of the 31st International Workshop on Statistical Modelling, Rennes, France, 4-8 July, 2016
