{ "background": "Public health surveillance systems are critical for disease control, yet their adoption across diverse health facilities in low-resource settings remains poorly quantified. Current evaluations often rely on binary metrics that fail to capture the complex, multi-level determinants of implementation success, limiting the utility of data for decision-making.", "purpose and objectives": "This protocol outlines a methodological framework to evaluate the adoption of integrated disease surveillance and response (IDSR) systems in Uganda. The primary objective is to develop and validate a Bayesian hierarchical model to estimate facility-level adoption rates and identify key predictors of successful implementation.", "methodology": "We will conduct a cross-sectional survey of a stratified random sample of health facilities. Data on structural, process, and outcome indicators of IDSR adoption will be collected. The core statistical model is a Bayesian hierarchical logistic regression: $\\text{logit}(p{ij}) = \\alpha + \\alpha{j[i]} + \\beta X{ij}$, where $p{ij}$ is the probability of full adoption for facility $i$ in district $j$, $\\alpha{j} \\sim N(0, \\sigma^2)$ are district-level random effects, and $X{ij}$ are facility-level covariates. Posterior distributions will be estimated using Markov chain Monte Carlo sampling.", "findings": "As this is a protocol, no empirical findings are presented. The anticipated analysis will generate district-level posterior estimates of adoption rates, expected to show significant heterogeneity (e.g., an interquartile range of 20-60%). The model will quantify the probability that specific factors, such as staff training completeness, increase the odds of adoption.", "conclusion": "This protocol proposes a novel analytical approach for surveillance system evaluation. The model's output will provide a more nuanced, probabilistic understanding of adoption, moving beyond descriptive summaries to actionable inference for health system strengthening.", "recommendations": "Future research should apply this modelling framework longitudinally to assess changes in adoption. Programme managers should utilise the probabilistic outputs to prioritise districts