Bayesian inference for a susceptible-exposed-infected-recovered epidemic model with data augmentation

Chouaib Beldjoudi, Tewfik Kernane and Hamid El Maroufy

Mathematical Population Studies, 2020, vol. 27, issue 4, 232-258

Abstract: A Bayesian data-augmentation method allows estimating the parameters in a susceptible-exposed-infected-recovered (SEIR) epidemic model, which is formulated as a continuous-time Markov process and approximated by a diffusion process using the convergence of the master equation. The estimation was carried out with latent data points between every pair of observations simulated through the Euler-Maruyama scheme, which involves imputing the missing data in addition to the model parameters. The missing data and parameters are treated as random variables, and a Markov-chain Monte-Carlo algorithm updates the missing data and the parameter values. Numerical simulations show the effectiveness of the proposed Markov-chain Monte-Carlo algorithm.

Date: 2020
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DOI: 10.1080/08898480.2019.1656491

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