Bayesian Hierarchical Model for Assessing System Reliability in Public Health Surveillance Systems: A Methodological Evaluation in Rwanda

Public health surveillance systems are critical for monitoring infectious diseases in real-time. In Rwanda, these systems aim to detect and respond quickly to outbreaks efficiently. A Bayesian hierarchical model was employed to assess system reliability across different regions. Data from patient records were used for analysis. The model revealed significant variations in system performance across provinces, with some areas showing higher detection rates than others. This study demonstrates the effectiveness of the proposed method in evaluating surveillance systems and highlights regional disparities. Further research should focus on implementing tailored strategies to improve the reliability of surveillance systems in underserved regions. Bayesian hierarchical model, public health surveillance, system reliability, Rwanda Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.