{ "background": "Public health surveillance systems are critical for disease control, yet robust methodologies for evaluating their adoption and long-term performance in low-resource settings are lacking. Ethiopia has implemented several such systems, but their sustained uptake and operational efficiency require formal assessment.", "purpose and objectives": "This case study aimed to develop and apply a novel time-series forecasting model to methodologically evaluate the adoption trajectory of key public health surveillance systems in Ethiopia, providing a replicable framework for performance measurement.", "methodology": "We constructed a hybrid forecasting model integrating an autoregressive integrated moving average (ARIMA) component with intervention analysis: $Yt = \\mu + \\phi Y{t-1} + \\theta \\epsilon{t-1} + \\beta It + \\epsilont$, where $It$ represents intervention dummies for policy changes. Model parameters were estimated using maximum likelihood, and forecast uncertainty was quantified with 95% prediction intervals. Historical administrative data on system utilisation formed the basis for projections.", "findings": "The model forecasts a significant deceleration in the adoption rate, plateauing at an estimated 67% (95% PI: 62, 72) coverage by the end of the forecast horizon. This suggests that without renewed intervention, current systems will not achieve universal adoption within existing structures. The analysis identified infrastructural constraints and training gaps as primary determinants of this trajectory.", "conclusion": "The methodological approach demonstrates that time-series forecasting provides a rigorous, quantitative tool for evaluating public health surveillance system performance beyond simple descriptive metrics, revealing critical limitations in current adoption pathways.", "recommendations": "Implement targeted, data-driven refresher training programmes and invest in peripheral health infrastructure to address forecasted stagnation. Policymakers should institutionalise similar forecasting evaluations for other health information systems to guide resource allocation.", "key words": "surveillance evaluation, adoption modelling, time-series analysis, health information systems, forecasting, sub-Saharan Africa", "contribution statement": "This study provides a novel methodological framework