Bayesian Inference for SIR Epidemic Model with dependent parameters

Abstract

This paper is concerned with the Bayesian inference for the dependent parameters of stochastic SIR epidemic model in a closed population. The estimation framework involves the introduction of m − 1 latent data between every pair of observations. Kibble’s bivariate gamma distribution is considered as a good candidate prior density of parameters, they give an appropriate frame to model the dependence between the parameters. A Markov chain Monte Carlo methods are then used to sample the posterior distribution of the model parameters. Simulated datasets are used to illustrate the proposed methodology.

Keywords

• bayesian inference
• kibble’s bivariate gamma distribution
• sir epidemics model
• mcmc methods
• 62f15
• 60e05
• 60j10
• 60j60
• 92d30
• 65c05