What question did this study set out to answer?

This research aims to develop a forecasting model to assess the efficiency of Rwanda's public health surveillance systems over time.

March 13, 2026Open Access

Longitudinal Evaluation of Public Health Surveillance Systems in Rwanda: A Time-Series Forecasting Model for Efficiency Optimisation, 2000–2026

Key Points

This research aims to develop a forecasting model to assess the efficiency of Rwanda's public health surveillance systems over time.
Utilized a longitudinal study design to analyze national-level surveillance performance data.
Employed a seasonal autoregressive integrated moving average (SARIMA) model for forecasting.
Estimated model parameters using maximum likelihood methods.
Quantified forecast uncertainty with 95% prediction intervals.
Forecasted a mean reduction in reporting latency of approximately 22% over the evaluation period.
Observed a sustained positive trend in system efficiency indicators.
Significant narrowing of prediction intervals for key efficiency metrics in later model periods.

Abstract

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 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.

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