Public health surveillance systems are crucial for monitoring disease outbreaks and ensuring timely intervention in Uganda. A Bayesian hierarchical model was developed to evaluate the performance of surveillance data across different regions. The model accounts for spatial and temporal variability and incorporates expert knowledge on disease prevalence and detection rates. The model revealed significant spatial heterogeneity in surveillance efficiency, with some regions showing a 20% higher detection rate compared to others. The Bayesian hierarchical model provides insights into the strengths and weaknesses of public health surveillance systems in Uganda, highlighting areas that require improvement. Public health authorities should prioritise resource allocation based on the model's findings to enhance surveillance efficiency across all regions. Bayesian Hierarchical Model, Public Health Surveillance, Efficiency Assessment, Uganda Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Kagyiya et al. (Mon,) studied this question.