Bayesian hierarchical modeling of Mpox in the African region (2022–2024): Addressing zero-inflation and spatial autocorrelation

Abstract

Mpox remains a signi_cant public health challenge in endemic regions of Africa. Understanding its spatial distribution and identifying key drivers in high-risk countries is critical for guiding e_ective interventions. This study applies a Zero-Inated Poisson (ZIP) model with spatial autocorrelation to estimate the adjusted relative risk (RR) of Mpox incidence across 24 African countries, strati_ed by Human Development Index (HDI) levels. The model accounts for overdispersion and excess zeros by incorporating spatial random e_ects and socio-environmental covariates, and was validated through model diagnostics and sensitivity analysis, demonstrating robustness of results. Spatial analysis revealed substantial heterogeneity in Mpox incidence, with elevated risk in the Democratic Republic of Congo (DRC), Nigeria, and Central African Republic (CAR) persisting after covariate adjustment (p < 0:001). Higher HDI levels were inversely associated with Mpox risk, with HDI quintile Q4 (very high HDI) showing a signi _cant reduction (aRR = 0.431; 95 % CrI: 0.099{0.724). Protective factors in low-risk areas included increased life expectancy at birth (aRR = 0.768; 95 % CrI: 0.688{0.892), higher educational attainment (aRR = 0.774; 95 % CrI: 0.680{0.921), nonlinear increases in gross national income (GNI) per capita, and a greater density of skilled health workers (aRR = 0.788; 95 % CrI: 0.701{0.934). Conversely, higher urban density was associated with increased Mpox risk, underscoring the inuence of population clustering on transmission dynamics. Notably, statistically signi_cant elevated-risk areas persisted in endemic countries of Western and Central Africa after covariate adjustment (p < 0:001). In contrast, previously undetected risk emerged in parts of Southern and Eastern Africa post-adjustment, revealing latent patterns obscured in the crude analysis (p < 0:001). Exceedance probability maps identi_ed countries with P(RR > 1) > 0.9 as priority areas for intensi_ed surveillance and targeted intervention. These patterns were not fully explained by the included covariates, suggesting the inuence of unmeasured factors such as environmental and climate variability, zoonotic reservoirs, or human{animal interactions. Further research is needed to deepen understanding of Mpox epidemiology and support locally tailored interventions.