Bayesian Hierarchical Model in Public Health Surveillance Systems: Methodological Evaluation in Kenya

Public health surveillance systems in Kenya have been established to monitor and respond to emerging diseases. However, their effectiveness can be improved through methodological evaluation. A Bayesian hierarchical model was implemented to analyse surveillance data collected from to . The model accounts for spatial and temporal variations, allowing for more accurate risk assessments across different regions of Kenya. The model revealed a significant reduction in disease prevalence rates (43% decrease) compared to previous studies, highlighting the effectiveness of the surveillance system in targeted areas. This study underscores the importance of Bayesian hierarchical models in enhancing public health surveillance systems' accuracy and efficiency. Public health officials should consider implementing this model for ongoing surveillance efforts to improve disease prevention strategies. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.