{ "background": "Public health surveillance systems in Rwanda have undergone significant development, yet methodological frameworks for quantitatively evaluating their yield—defined as the actionable information generated per unit resource—are lacking. This gap impedes the optimisation of system performance and resource allocation for future health threats.", "purpose and objectives": "This protocol details a study to methodologically evaluate the yield of Rwanda's integrated disease surveillance system and to develop a robust time-series forecasting model for yield improvement. The primary objective is to generate a validated predictive tool for public health planning.", "methodology": "We will conduct a longitudinal analysis of national surveillance data. The core forecasting model is a seasonal autoregressive integrated moving average (SARIMA) formulation: $Yt = \\mu + \\phi1 Y{t-1} + \\Theta{12} Y{t-12} + \\epsilont$, where $Y_t$ is the yield metric. Model parameters will be estimated using maximum likelihood, and forecast uncertainty will be quantified with 95% prediction intervals. Robust standard errors will be employed to account for potential heteroscedasticity in the time series.", "findings": "As this is a protocol, no empirical findings are presented. The anticipated outcome is a calibrated SARIMA model capable of forecasting surveillance yield. A key output will be the projected direction and magnitude of yield change, such as a forecasted percentage increase or decrease over a specified future horizon, accompanied by its prediction interval.", "conclusion": "The developed model is expected to provide a novel, quantitative evidence base for strengthening surveillance efficiency. It will shift evaluation from descriptive reporting to predictive analytics.", "recommendations": "We recommend the integration of the forecasting model into the national health management information system for routine performance monitoring. Future research should adapt the methodology for sub-national granularity.", "key words": "public health surveillance, yield, forecasting, time-series analysis, SARIMA, health systems strengthening, evaluation methodology", "contribution statement": "This protocol introduces a novel application of SARIMA