Abstract
{ "background": "Public health surveillance systems are critical for early detection and response to disease outbreaks. In Uganda, the optimisation of these systems through robust methodological evaluation and forecasting remains a significant challenge, impacting the efficacy of risk reduction strategies.", "purpose and objectives": "This systematic review aims to critically evaluate methodological approaches used in the assessment of public health surveillance systems in Uganda and to synthesise evidence on the application of time-series forecasting models for measuring risk reduction.", "methodology": "A systematic search of multiple electronic databases was conducted following PRISMA guidelines. Studies were screened against pre-defined inclusion criteria, with data extracted and synthesised narratively. Methodological quality was appraised using appropriate tools. The core forecasting model evaluated was an ARIMA formulation: $Xt = c + \\sum{i=1}^{p}\\phii X{t-i} + \\sum{i=1}^{q}\\thetai \\epsilon{t-i} + \\epsilont$, where parameter uncertainty was assessed via 95% confidence intervals.", "findings": "The review identified a predominant focus on malaria and influenza-like illness surveillance. A key finding was that approximately 60% of the evaluated studies employing forecasting models utilised ARIMA or its variants, though model performance was heterogeneous, with prediction intervals often widening substantially beyond short-term horizons, indicating high uncertainty in long-range forecasts.", "conclusion": "Methodological rigour in surveillance system evaluation is variable, and while time-series forecasting shows promise for specific diseases, its utility for comprehensive system optimisation and risk measurement is constrained by data quality and contextual integration challenges.", "recommendations": "Future work should prioritise the development of integrated, multi-disease surveillance frameworks with improved data granularity. Investment is needed in capacity building for advanced analytical techniques and in validating forecasting models against field-based intervention outcomes.", "key words": "public health surveillance, forecasting, time-series analysis, system evaluation, Uganda, risk reduction", "contribution statement": "This review provides the first consolidated methodological critique of surveillance system