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Assess the potential of time-series models for forecasting yield improvement in community health centres in Uganda.

March 4, 2026Open Access

Forecasting Yield Improvement in Community Health Centres Systems Using Time-Series Models in Uganda: A Methodological Evaluation

Key Points

Assess the potential of time-series models for forecasting yield improvement in community health centres in Uganda.
Employed ARIMA (AutoRegressive Integrated Moving Average) model for forecasting yield outcomes.
Analyzed historical data from selected community health centres.
Estimated treatment effect using logistic regression model.
ARIMA model predicted a 15% increase in service delivery capacity over two years.
Uncertainty range reported as ±3% for the predictions.
Model demonstrates potential for enhancing operational planning and resource allocation.

Abstract

Community health centres in Uganda are pivotal for delivering healthcare services to underserved populations. However, their operational efficiency can be improved through better resource management and forecasting tools. The study employed ARIMA (AutoRegressive Integrated Moving Average) model to forecast yield outcomes based on historical data from selected health centres. ARIMA model predicted a significant increase of 15% in service delivery capacity with an uncertainty range of ±3% over the next two years. The ARIMA model demonstrated its potential for enhancing operational planning and resource allocation within community health centre systems. Communities and policymakers should consider implementing time-series forecasting models to optimise healthcare delivery in underserved regions. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.

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