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 logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Katooseko et al. (Tue,) studied this question.