Bayesian Inference for SIR Epidemic Model with dependent parameters

Moroccan Journal of Pure and Applied Analysis

Zbair Mokhtar¹  Qaffou Abdelaziz¹  Maroufy Hamid El² 

[1] Applied Mathematics and Scientific Computing Laboratory, Department of Applied Mathematics, Faculty of Sciences and Techniques, C.P:523, Sultan Moulay Slimane University-Beni-Mellal, Morocco.;Department of Applied Mathematics, Faculty of Sciences and Techniques, C.P:523, Sultan Moulay Slimane University-Beni-Mellal, Morocco.;

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

DOI  :  10.2478/mjpaa-2022-0017

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown