What question did this study set out to answer?

This research aims to evaluate and forecast the reliability of Ethiopia's public health surveillance systems using time-series analysis.

March 13, 2026Open Access

Methodological Evaluation and Time-Series Forecasting for Reliability Assessment of Public Health Surveillance Systems in Ethiopia, 2000–2026

Key Points

This research aims to evaluate and forecast the reliability of Ethiopia's public health surveillance systems using time-series analysis.
Developed a SARIMA model for forecasting system performance.
Conducted an intervention study analyzing longitudinal surveillance data.
Estimated model parameters using maximum likelihood techniques.
The fitted SARIMA model indicates a significant negative trend in system timeliness.
Forecast predicts a 15.2 percentage point decline in on-time reporting over the projection period.
Prediction interval width indicates increased forecast uncertainty beyond immediate periods.

Abstract

Public health surveillance systems in Ethiopia have undergone significant structural changes, yet their operational reliability remains methodologically under-evaluated. A robust, quantitative framework for forecasting system performance is absent, limiting proactive interventions. This study aimed to develop and validate a time-series forecasting model to assess the reliability of the national surveillance system and to project future performance under current operational conditions. We conducted an intervention study analysing longitudinal surveillance data. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model, specified as SARIMA (p, d, q) (P, D, Q) ₛ, was fitted to historical completeness and timeliness metrics. Model parameters were estimated using maximum likelihood, and forecasts were generated with 95% prediction intervals. The fitted SARIMA (1, 1, 1) (0, 1, 1) ₁2 model indicated a significant negative trend in system timeliness, with a forecasted decline in on-time reporting of 15. 2 percentage points over the projection period. Forecast uncertainty, represented by prediction interval width, increased substantially beyond the immediate forecast horizon. The surveillance system exhibits a statistically significant decline in reliability, which is projected to continue without intervention. The forecasting model provides a novel tool for pre-emptive system assessment. Implement the forecasting methodology for routine monitoring and allocate resources to districts identified as high-risk for reporting failures. Future work should integrate environmental and agricultural covariates to enhance model specificity. surveillance, forecasting, reliability, SARIMA, public health, Ethiopia This paper introduces a novel application of SARIMA modelling for the predictive reliability assessment of public health surveillance, providing a quantitative tool for pre-emptive system strengthening.

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