Methodological Evaluation and Time-Series Forecasting of Public Health Surveillance System Adoption in Nigeria, 2000–2026

{ "background": "Public health surveillance systems are critical for disease control, yet their adoption across Nigeria remains uneven and inadequately quantified. Existing evaluations are often cross-sectional, lacking the longitudinal rigour needed to forecast trends and inform strategic investment. ", "purpose and objectives": "This protocol details a methodological evaluation and the development of a time-series forecasting model to measure and predict the adoption rates of integrated disease surveillance and response (IDSR) systems. The primary objective is to generate robust, forward-looking evidence to guide policy. ", "methodology": "We will conduct a retrospective analysis of national and state-level surveillance adoption data. A seasonal autoregressive integrated moving average (SARIMA) model, specified as \ (B) \ (Bˢ) \ᵈ\D yt = \ (B) \ (Bˢ) \ₜ, will be fitted to the historical data. Model diagnostics will include checks for residual autocorrelation using the Ljung-Box test, with forecasts generated alongside 95% prediction intervals to quantify uncertainty. ", "findings": "As this is a protocol, no empirical findings are presented. The anticipated output of the completed research will be a validated forecasting model projecting state-level adoption rates. A key expected result is the identification of a significant positive temporal trend, with model forecasts suggesting a potential increase in national adoption of at least 15 percentage points over the forecast horizon. ", "conclusion": "The proposed methodology will provide a novel, evidence-based tool for assessing the trajectory of surveillance system adoption, moving beyond descriptive evaluation towards predictive analytics. ", "recommendations": "Future research should integrate this forecasting approach with socio-economic covariates to identify determinants of adoption. Policymakers should utilise such models for targeted resource allocation and to monitor progress towards national coverage targets. ", "key words": "public health surveillance, forecasting, time-series analysis, health systems research, Nigeria", "contribution statement": "This protocol introduces a novel application of SARIMA modelling to forecast public health surveillance adoption, generating a replicable methodological framework for