{ "background": "Public health surveillance systems are critical for disease control, yet their adoption across diverse administrative regions in Nigeria remains uneven and poorly quantified. Existing evaluations often lack a formal statistical framework to integrate multi-level data and account for spatial and temporal heterogeneity.", "purpose and objectives": "This study aimed to develop and apply a novel Bayesian hierarchical model to evaluate the adoption rates of integrated disease surveillance and response (IDSR) systems across Nigerian states, and to project future trajectories under current policy conditions.", "methodology": "We constructed a Bayesian hierarchical logistic model using state-level panel data on surveillance system implementation. The core model is specified as $\\text{logit}(p{it}) = \\alpha + \\beta X{it} + \\gammat + \\deltai + \\epsilon{it}$, where $p{it}$ is the probability of full adoption in state $i$ at time $t$, $X{it}$ are covariates, $\\gammat$ captures temporal trends, and $\\delta_i$ are state-level random effects. Parameters were estimated using Hamiltonian Monte Carlo.", "findings": "Model projections indicate a median national adoption rate of 67% (95% credible interval: 61–73%) by the end of the projection period, with substantial inter-state variation. The posterior probability that the adoption rate in northern states lags behind southern states exceeded 0.85.", "conclusion": "The methodological approach provides a robust, probabilistic framework for evaluating public health system adoption. Findings reveal that, without intervention, significant geographical inequities in surveillance capacity will persist.", "recommendations": "Policy should prioritise targeted, data-driven support for low-adoption regions. Future evaluations of health systems should employ similar hierarchical models to formally incorporate uncertainty and heterogeneity.", "key words": "Bayesian statistics, hierarchical model, public health surveillance, health systems, adoption, Nigeria", "contribution statement": "This paper introduces a novel Bayesian hierarchical modelling framework for the spatio-temporal analysis of health system adoption