Bayesian Hierarchical Model Assessment of Public Health Surveillance Systems in Ethiopia

Public health surveillance systems in Ethiopia have been established to monitor diseases and track their prevalence over time. A Bayesian hierarchical model was utilised to analyse the performance of public health surveillance systems across different regions in Ethiopia. The model accounts for variability within and between regions. The model revealed significant regional variations in system reliability, with some areas showing higher accuracy rates than others (e. g. , Region A had a mean accuracy rate of 85% compared to Region B's 70%). Bayesian hierarchical models provide a robust framework for evaluating public health surveillance systems and identifying regions requiring improvement. Policy recommendations include targeted interventions in areas with lower system reliability, such as enhancing training programmes or improving infrastructure in Region A. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.