Bayesian Hierarchical Model for Assessing Risk Reduction in Public Health Surveillance Systems in Ethiopia

Public health surveillance systems in Ethiopia have been established to monitor disease outbreaks, but their effectiveness is variable. A Bayesian hierarchical model can enhance understanding and improvement of these systems. A Bayesian hierarchical model was applied to analyse data from PHSS records. This approach allowed for the integration of local and national surveillance data to quantify risk reduction effects across different regions. The analysis revealed a significant proportion (35%) decrease in reported disease outbreaks when using the Bayesian hierarchical model compared to traditional methods, highlighting its effectiveness in risk assessment. This study demonstrated that the Bayesian hierarchical model can effectively enhance the accuracy and reliability of public health surveillance systems in Ethiopia, contributing to more efficient outbreak detection and management. Public health authorities should consider implementing this model within their PHSS for improved disease monitoring and response strategies. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.