<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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 <italic>m</italic> − 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.</p>
</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.

Bayesian Inference for SIR Epidemic Model with dependent parameters

Applied Mathematics and Scientific Computing Laboratory

Department of Applied Mathematics, Faculty of Sciences and Techniques

C.P:523, Sultan Moulay Slimane University-Beni-Mellal

Moroccan Journal of Pure and Applied Analysis

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Bayesian Inference for SIR Epidemic Model with dependent parameters"}

This paper is concerned with the Bayesian inference for the dependent parameters of stochastic SIR epidemic model in a closed population. The estimation...

Bayesian Inference for SIR Epidemic Model with dependent parameters

Online veröffentlicht: 28. Mai 2022

Seitenbereich: 244 - 255

Eingereicht: 10. Feb. 2021

Akzeptiert: 03. Apr. 2022

DOI: https://doi.org/10.2478/mjpaa-2022-0017

Schlüsselwörter
Bayesian inference, Kibble’s bivariate gamma distribution, SIR epidemics model, MCMC methods

© 2022 Abdelaziz Qaffou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Bayesian Inference for SIR Epidemic Model with dependent parameters

Online veröffentlicht: 28. Mai 2022

Seitenbereich: 244 - 255

Eingereicht: 10. Feb. 2021

Akzeptiert: 03. Apr. 2022

DOI: https://doi.org/10.2478/mjpaa-2022-0017

SchlüsselwörterBayesian inference, Kibble’s bivariate gamma distribution, SIR epidemics model, MCMC methods

© 2022 Abdelaziz Qaffou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Bayesian inference, Kibble’s bivariate gamma distribution, SIR epidemics model, MCMC methods