Public health surveillance in Tanzania has been a focus for improving disease detection and response efficiency. A Bayesian hierarchical model was applied to analyse surveillance data, aiming to quantify the system's performance gains over time. The model revealed an average efficiency gain of 15% in disease detection compared to previous methods. Bayesian hierarchical modelling provided a robust framework for evaluating public health surveillance systems and their improvements. Public health authorities should consider implementing similar models to enhance future surveillance efforts. Bayesian Hierarchical Model, Public Health Surveillance, Efficiency Gain, Tanzania Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.