Public health surveillance systems in Uganda have been established to monitor diseases and provide early warnings for disease outbreaks. A Bayesian hierarchical model was applied to analyse data on system adoption rates from multiple sites in Uganda, incorporating uncertainty through robust standard errors. The analysis revealed that the adoption rate varied significantly across different regions of Uganda, with some areas having an adoption rate as high as 85% among healthcare workers. The Bayesian hierarchical model provided a nuanced understanding of system adoption rates and highlighted regional disparities in its implementation. Future studies should consider factors influencing the adoption rates to improve public health surveillance systems' effectiveness. Bayesian Hierarchical Model, Public Health Surveillance Systems, Adoption Rates, Uganda Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Mugyenyi et al. (Sun,) studied this question.