Methodological Assessment and Risk Reduction Evaluation of Public Health Surveillance Systems in Uganda Using Bayesian Hierarchical Models

Public health surveillance systems in Uganda are essential for monitoring diseases and implementing effective interventions. A comprehensive review of existing literature on public health surveillance systems, focusing on methodologies used in Uganda. The study employed Bayesian hierarchical models for analysis. Bayesian hierarchical models showed a significant reduction (p < 0.05) in the risk of disease outbreak when implemented correctly across multiple regions. The effectiveness of Bayesian hierarchical models was robust and adaptable to various surveillance needs, providing a reliable framework for future interventions. Public health officials should prioritise methodological training and resource allocation to ensure optimal performance of surveillance systems. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.