What question did this study set out to answer?

The study aims to assess the forecasting power of a national surveillance system using a time-series model.

March 13, 2026Open Access

Evaluating Surveillance Systems in Tanzania: A Time-Series Forecasting Model for Clinical Outcomes, 2000–2026

Key Points

The study aims to assess the forecasting power of a national surveillance system using a time-series model.
Constructed a univariate forecasting model with historical surveillance data.
Utilized a SARIMA model for time-series forecasting.
Minimized the Akaike Information Criterion for model selection.
Quantified forecast uncertainty using 95% prediction intervals.
The model achieved strong in-sample and out-of-sample forecasting accuracy.
Projected an 18% increase in annual caseload over the forecast horizon.
Identified systemic gaps in data granularity that affect forecast precision.

Abstract

{ "background": "Public health surveillance systems in sub-Saharan Africa generate vast data, yet their methodological rigour for forecasting clinical burdens is often under-evaluated, limiting proactive resource allocation. ", "purpose and objectives": "This case study aimed to methodologically evaluate the forecasting capability of a national surveillance system by developing and validating a time-series model for predicting key clinical outcomes. ", "methodology": "We constructed a univariate forecasting model using historical, de-identified surveillance data. The core model was a seasonal autoregressive integrated moving average (SARIMA) formulation: yt = \ + \ (Bˢ) \ (B) (1-B) ᵈ (1-Bˢ) D yt + \ (Bˢ) \ (B) \, where \ \ N (0, \²). Model selection was based on minimising the Akaike Information Criterion, with forecast uncertainty quantified using 95% prediction intervals. ", "findings": "The model demonstrated robust in-sample fit and out-of-sample forecasting accuracy for a major clinical indicator. A key finding was a projected 18% increase (95% prediction interval: 12% to 24%) in the annual caseload over the forecast horizon, highlighting a significant upward trend. The evaluation revealed specific systemic gaps in data granularity affecting forecast precision. ", "conclusion": "The surveillance system provides a viable foundation for forecasting, but its utility is constrained by data quality and completeness issues. Methodological evaluation through modelling exposes critical points for system strengthening. ", "recommendations": "Integrate routine forecasting audits into surveillance system evaluations. Invest in improving the temporal resolution and clinical detail of reported data to enhance model accuracy and public health utility. ", "key words": "surveillance evaluation, time-series forecasting, SARIMA, public health informatics, health systems, predictive modelling", "contribution statement": "This study provides a novel methodological framework for the quantitative evaluation of public health surveillance systems' forecasting performance, moving beyond descriptive assessments

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