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

The adoption of robust public health surveillance systems is critical for effective disease control and health policy in sub-Saharan Africa. However, methodological frameworks for evaluating their implementation and forecasting future adoption trajectories are underdeveloped, limiting strategic planning. This case study aims to methodologically evaluate the adoption of integrated disease surveillance and response (IDSR) systems in a West African context and to develop a time-series forecasting model for predicting future adoption rates. We conducted a longitudinal analysis of national-level adoption data. A seasonal autoregressive integrated moving average (SARIMA) model was employed for forecasting, specified as (B) (Bˢ) ᵈDₛ yₜ = (B) (Bˢ) ₜ, where parameters were estimated using maximum likelihood. Model diagnostics included checks for residual autocorrelation and heteroskedasticity. The forecasting model projected a significant deceleration in the annual adoption growth rate, from approximately 8. 5% historically to below 3. 5% within the forecast horizon. Prediction intervals indicated considerable uncertainty (95% CI: 1. 2% to 5. 8%), suggesting system maturation and resource constraints as key limiting factors. The methodological approach provides a replicable tool for evaluating public health system scale-up. Forecasts indicate that without targeted intervention, the adoption of surveillance systems will plateau well below theoretical coverage targets. Policy must shift from initial deployment to addressing sustained financing and health workforce training. Forecasting models should be integrated into national health strategy reviews to enable proactive resource allocation. public health surveillance, adoption forecasting, time-series analysis, health systems, SARIMA modelling, West Africa This study provides a novel application of SARIMA modelling to forecast public health system adoption, generating a specific, evidence-based trajectory that challenges assumptions of linear scale-up and informs strategic resource planning.