Bayesian Hierarchical Model for Assessing Reliability of Public Health Surveillance Systems in Tanzania

Public health surveillance systems are crucial for monitoring diseases in endemic regions like Tanzania. However, their reliability can vary significantly across different settings. We employed a Bayesian hierarchical model to assess system reliability, accounting for intra-cluster variability within regions. The model was applied to data from multiple years in Tanzania's public health surveillance records. Our analysis revealed a moderate level of reliability in the surveillance systems across different geographical areas (e. g. , 72% confidence interval for regional consistency). The Bayesian hierarchical model provided nuanced insights into system performance, which can inform future improvements and resource allocation in public health surveillance. Public health officials should consider implementing targeted interventions to enhance the reliability of their systems where they are currently below threshold levels. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.