{ "background": "The adoption of public health surveillance systems in low-resource settings is critical for food security and disease control, yet robust methodological frameworks for evaluating their uptake are lacking. Existing approaches often fail to account for hierarchical data structures and uncertainty inherent in longitudinal implementation studies.", "purpose and objectives": "This study aimed to develop and apply a novel Bayesian hierarchical model to evaluate the adoption rates of a nationwide electronic integrated disease surveillance and response system. The primary objective was to quantify temporal and spatial heterogeneity in adoption across administrative districts.", "methodology": "We conducted an intervention study, analysing longitudinal adoption data from health facilities. The core model was specified as $\\text{logit}(p{ijt}) = \\alpha + \\beta X{ijt} + ui + v{jt}$, where $p{ijt}$ is the probability of adoption for facility $i$ in district $j$ at time $t$, $X{ijt}$ are covariates, $ui$ are facility-level random effects, and $v{jt}$ are spatio-temporal district random effects. Inference was based on posterior distributions with 95% credible intervals.", "findings": "The model estimated a strong positive temporal trend, with the national adoption rate increasing from an estimated 12% to 78% over the study period. Posterior probability indicated a greater than 0.99 chance that the intervention had a positive effect. District-level random effects revealed significant geographical heterogeneity, with some regions lagging by an estimated 15-20 percentage points.", "conclusion": "The Bayesian hierarchical model provided a robust, probabilistic framework for evaluating surveillance system adoption, effectively quantifying both overall progress and sub-national disparities. The intervention was successful in driving widespread uptake.", "recommendations": "Programme managers should utilise this modelling approach for real-time, granular performance evaluation. Resources should be prioritised for districts with persistently low posterior adoption estimates to address equity gaps.", "key words": "Bayesian inference, hierarchical model, public health surveillance, adoption