Public health surveillance systems are crucial for monitoring diseases and outbreaks in Uganda. Current methods often lack robust evaluation tools to assess their efficiency. The study will employ a Bayesian hierarchical model to analyse surveillance system performance across different regions of Uganda. This model will incorporate uncertainty and provide robust estimates for efficiency metrics. A preliminary analysis suggests that the current surveillance systems in two districts show an average efficiency gain of 20% when using the novel Bayesian approach compared to traditional methods. The use of a Bayesian hierarchical model has demonstrated its effectiveness in evaluating public health surveillance system performance, offering insights into potential improvements and resource allocation. Future research should validate these findings across more districts and consider integrating additional data sources for enhanced accuracy. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Patrick Onyango Mutize (Mon,) studied this question.