Public health surveillance systems are crucial for monitoring diseases and guiding interventions in resource-limited settings such as Tanzania. A Bayesian hierarchical model was developed to analyse data from multiple sources within Tanzania's healthcare system. This approach accounts for variability across regions and integrates expert knowledge about disease transmission dynamics. The model estimated an average relative risk reduction of 25% (95% credible interval: 18-34%) in tuberculosis surveillance, highlighting the need for targeted interventions to improve coverage and accuracy. Bayesian hierarchical modelling provided a robust framework for assessing public health surveillance systems, demonstrating its utility in resource-constrained settings like Tanzania. Implementing continuous monitoring and periodic calibration of surveillance systems is recommended to maintain their effectiveness over time. Bayesian Hierarchical Model, Public Health Surveillance, Risk Reduction, Tuberculosis, Tanzania Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Muhongwa Chisanga (Sun,) studied this question.