Bayesian Inference for SIR Epidemic Model with dependent parameters-Reference-Cited by-同舟云学术

Bayesian Inference for SIR Epidemic Model with dependent parameters

Published:2022-05-01 Issue:2 Volume:8 Page:244-255
ISSN:2351-8227
Container-title:Moroccan Journal of Pure and Applied Analysis
language:en
Short-container-title:

Author:

Qaffou Abdelaziz¹,Maroufy Hamid El²,Zbair Mokhtar¹

Affiliation:

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 .

2. Department of Applied Mathematics, Faculty of Sciences and Techniques , C.P:523, Sultan Moulay Slimane University-Beni-Mellal , Morocco .

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Control and Optimization,Numerical Analysis,Analysis

Link

https://www.sciendo.com/pdf/10.2478/mjpaa-2022-0017

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