Bayesian Hierarchical Model for Evaluating Public Health Surveillance System Efficiency in Kenya,

Public health surveillance systems are crucial for monitoring disease prevalence and guiding preventive measures in populations. In Kenya, these systems have been operational since , with a focus on enhancing their efficiency to better serve public health needs. A Bayesian hierarchical model was employed to analyse data from the surveillance system in Kenya, spanning from to . This method accounts for variability at different levels of the hierarchy (e.g., individual cases versus aggregated regional data). The analysis revealed that the efficiency gains in the surveillance system were approximately 30% higher than previously estimated, with significant improvements observed in early detection rates. This study provides evidence that a Bayesian hierarchical model can effectively measure and enhance the efficiency of public health surveillance systems. The findings suggest substantial potential for further optimization to improve service delivery. Based on these results, it is recommended that resources be allocated towards enhancing early warning mechanisms and improving data collection methods within the Kenyan surveillance system. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.