Public health surveillance systems in Kenya are critical for monitoring disease outbreaks and ensuring timely interventions. A Bayesian hierarchical model was applied to assess surveillance system performance across different regions in Kenya. The model accounts for spatial and temporal variations in data. The model indicated that surveillance effectiveness varied significantly between regions, with some areas showing a 20% higher detection rate of infectious diseases compared to others. The Bayesian hierarchical model provides valuable insights into the performance variability of public health surveillance systems across Kenya's diverse geographical settings. Public health officials should prioritise resource allocation based on region-specific surveillance effectiveness data to optimise intervention strategies. Bayesian Hierarchical Model, Public Health Surveillance, Kenya, Spatial Analysis Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.