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The review critiques the methodological approaches to public health surveillance in Uganda and examines time-series forecasting models for risk reduction efficacy.

March 13, 2026Open Access

Methodological Evaluation and Time-Series Forecasting for Public Health Surveillance System Optimisation in Uganda: A Systematic Review

Key Points

The review critiques the methodological approaches to public health surveillance in Uganda and examines time-series forecasting models for risk reduction efficacy.
Conducted a systematic search of electronic databases following PRISMA guidelines.
Screened studies based on pre-defined inclusion criteria and extracted data narratively.
Appraised methodological quality using appropriate evaluation tools.
Evaluated ARIMA forecasting models for effectiveness in disease surveillance.
Predominant focus identified on malaria and influenza surveillance.
Around 60% of studies using forecasting models employed ARIMA or its variants.
Model performance showed significant variability, with prediction intervals widening for longer-term forecasts.
Highlighted challenges in data quality and contextual integration for comprehensive system optimisation.

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 + =1^{p\ Xt-i + =1^q\ -i + \, 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

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