Bayesian Hierarchical Model for Evaluating Public Health Surveillance System Reliability in Ethiopia: A Methodological Framework

Public health surveillance systems are essential for monitoring infectious diseases in Ethiopia. However, their reliability can be challenging to assess due to varying data quality and reporting practices. A Bayesian hierarchical model will be employed, incorporating data from multiple sources and accounting for heterogeneity among regions. Model parameters will be estimated using Markov Chain Monte Carlo methods, ensuring robust inference on system reliability. We anticipate that the Bayesian hierarchical model will provide more precise estimates of system performance compared to traditional models by leveraging available data and acknowledging regional differences. This protocol outlines a method for evaluating public health surveillance systems in Ethiopia using advanced statistical techniques, contributing to improved understanding and operational efficiency. The findings from this research should inform the design and improvement of future surveillance initiatives in Ethiopia. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.