Early-Stage Modelling and Forecast of COVID-19 Outbreak in Burkina Faso using a Bayesian SIR Approach

Abstract

In this article, we propose a Bayesian approach for estimating and predicting the magnitude of the coronavirus epidemic in Burkina Faso in its early stage. Our approach is inspired by the work of Wang et al. but adapted to the Burkinabe context. Two models are presented: a simple Bayesian SIR approach and another Bayesian SIR which takes into account the public health measures undertaken by the government of Burkina Faso. The approach was implemented at the early stage of the COVID-19 pandemic in Burkina Faso, covering the period from March 9 to April 30, 2020. The results of the analyses will allow a good prediction of COVID-19 infections and deaths in the early days of the epidemic, considering government policies.