Public health surveillance systems are critical for early disease detection and resource allocation, yet longitudinal assessments of their operational efficiency over extended periods are scarce, particularly in sub-Saharan Africa. This study aimed to develop and validate a time-series forecasting model to quantify longitudinal efficiency gains within Rwanda's integrated disease surveillance and response system, providing a methodological framework for optimising resource deployment. A longitudinal study design was employed, analysing national-level surveillance performance data. A seasonal autoregressive integrated moving average (SARIMA) model, specified as $\text{SARIMA}(p,d,q)(P,D,Q)_s$, was fitted to forecast key efficiency metrics. Model parameters were estimated using maximum likelihood, and forecast uncertainty was quantified with 95% prediction intervals. The forecasting model indicated a sustained positive trend in system efficiency, with a forecasted mean reduction in reporting latency of approximately 22% over the forecast horizon. Prediction intervals for key efficiency metrics narrowed significantly in later model periods, suggesting improved system stability. The applied time-series model provides a robust, quantitative tool for tracking the longitudinal performance of public health surveillance, demonstrating measurable efficiency improvements within the studied system. Implement the forecasting framework for routine performance monitoring and proactive resource planning. Future research should integrate cost data to evaluate the economic impact of efficiency gains. surveillance systems, time-series analysis, forecasting, operational efficiency, public health, longitudinal study This paper introduces a novel application of SARIMA modelling for the longitudinal, quantitative evaluation of surveillance system efficiency, providing a replicable methodological advance for health systems research.