Modeling the spread of Mpox viral disease in African countries using a Bayesian hierarchical model

Abstract

Mpox, a zoonotic disease similar to smallpox, has garnered increasing attention due to its sporadic outbreaks across different regions. This study employs a comprehensive statistical approach, combining Poisson regression, Generalized Linear Mixed Models (GLMM), and Bayesian Hierarchical Models (BHM) to analyze the spread of Mpox. The analysis accounts for regional variations in transmission dynamics and provides probabilistic estimates of key epidemiological parameters. Our findings reveal significant variability in the impact of covariates such as population density, healthcare capacity, and mobility on Mpox transmission. The Bayesian Hierarchical Model, in particular, offers a robust framework for understanding the complex transmission dynamics of the disease across diverse regions. These insights underscore the necessity of region-specific public health strategies to effectively control and prevent Monkeypox outbreaks.