"background": "Public health surveillance systems in Uganda face persistent methodological challenges, including fragmented data streams, inconsistent case definitions, and variable reporting completeness. These issues compromise the timeliness and accuracy of outbreak detection and resource allocation. A robust, quantitative framework for the systematic evaluation and optimisation of these systems is critically needed. ", "purpose and objectives": "This protocol details the development and application of a novel Bayesian hierarchical modelling framework to methodologically evaluate and optimise the efficiency of public health surveillance systems. The primary objective is to quantify efficiency gains from hypothetical system improvements, such as integrating laboratory and community-based reporting. ", "methodology": "We will construct a Bayesian hierarchical model to analyse longitudinal surveillance data on notifiable diseases. The core model structure is y{it \ (), with \ () = \ + \ Xit + ui + vt, where ui and vt are structured spatial and temporal random effects. Model parameters will be estimated using Hamiltonian Monte Carlo, with posterior predictive checks used to assess fit. Efficiency will be measured via the posterior distribution of the expected case detection rate. ", "findings": "As this is a protocol, no empirical findings are presented. The anticipated output is a validated modelling framework capable of generating quantitative estimates. For instance, we expect to produce posterior probabilities that a proposed integration intervention would improve the system's sensitivity by a specified proportion, such as 15%. ", "conclusion": "The proposed framework represents a significant methodological advance for the evidence-based assessment of surveillance systems. It will provide a reproducible tool for quantifying the potential impact of system modifications before implementation. ", "recommendations": "We recommend the adoption of this Bayesian framework by national health authorities for the periodic technical audit of surveillance systems. Future research should focus on adapting the model for real-time performance monitoring. ", "key words": "Bayesian hierarchical model, public health surveillance, health systems research, methodological evaluation, Uganda
Nakato Muwanga (Thu,) studied this question.