Bayesian Hierarchical Model for Evaluating Public Health Surveillance System Reliability in Tanzania,

Public health surveillance systems are crucial for monitoring infectious diseases in developing countries like Tanzania. These systems rely on reports from healthcare providers and laboratories to detect outbreaks early. A Bayesian hierarchical model was applied to analyse surveillance data, accounting for variability at different levels (e.g., individual healthcare providers, regional laboratories). The model estimated that the reporting rate from healthcare providers varied by up to 25% across regions, with a mean of 80%, indicating moderate reliability. Bayesian hierarchical modelling provided insights into system variability and reliability, suggesting areas for improvement in data collection and transmission processes. Enhancements should focus on standardising reporting protocols and increasing training to ensure consistent and timely reports from healthcare providers. Public health surveillance, Bayesian hierarchical model, System Reliability, Tanzania Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.