{ "background": "Public health surveillance systems are critical for disease control, yet their reliability in resource-limited settings is often uncertain. Methodological evaluations of these systems are required to quantify their performance and guide improvements.", "purpose and objectives": "This study aimed to develop and apply a novel Bayesian hierarchical model to evaluate the reliability of national public health surveillance systems, using Kenya as a case study. The objective was to quantify system completeness and timeliness while accounting for spatial and temporal heterogeneity.", "methodology": "We developed a Bayesian hierarchical model integrating reported surveillance data with covariates including healthcare access and population density. The core model for the probability of a true event being reported in district $i$ and month $t$ is: $\\text{logit}(p{it}) = \\alpha + \\beta X{it} + ui + vt$, where $ui \\sim N(0, \\sigmau^2)$ and $vt \\sim N(0, \\sigmav^2)$ are random effects. Model parameters were estimated using Markov chain Monte Carlo simulation.", "findings": "The model identified substantial spatial variation in system reliability, with posterior probabilities of reporting completeness below 0.6 in over 30% of districts. The estimated national annual reporting completeness had a 95% credible interval of 0.58 to 0.72, indicating significant under-reporting. Temporal random effects showed a declining trend in reliability during periods of health system strain.", "conclusion": "The methodological evaluation reveals that surveillance reliability in the study context is moderate and highly variable, compromising data utility for real-time response. The Bayesian hierarchical model provides a robust framework for quantifying this uncertainty.", "recommendations": "Implement routine reliability assessments using this modelling framework to prioritise districts for surveillance strengthening. Integrate model outputs into data interpretation to adjust for under-reporting in epidemiological analyses.", "key words": "Bayesian hierarchical model, surveillance evaluation, health systems, reliability, spatial statistics, disease reporting", "contribution