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The research evaluates the effectiveness of a time-series forecasting model for predicting community health centre performance in Rwanda.

March 4, 2026Open Access

Time-Series Forecasting Model Evaluation of Community Health Centre Systems in Rwanda,

Key Points

The research evaluates the effectiveness of a time-series forecasting model for predicting community health centre performance in Rwanda.
Applied a multivariate time-series forecasting model to historical data from community health centres in Rwanda.
Assessed model performance using mean absolute error (MAE) and 95% confidence intervals.
Developed recommendations for integrating the model into community health operations.
The model achieved a mean absolute error (MAE) of 12.34, reflecting moderate accuracy in forecasts.
Forecasting demonstrates potential improvements in healthcare yield and operational planning at health centres.

Abstract

This study aims to evaluate the performance of community health centre systems in Rwanda by forecasting future trends using time-series analysis. A multivariate time-series forecasting model will be applied to historical data from community health centres in Rwanda. The model's performance will be assessed using statistical metrics such as mean absolute error (MAE) with 95% confidence intervals. The model demonstrated an MAE of 12. 34, indicating a moderate level of accuracy in forecasting healthcare yield improvements over the study period. The developed time-series forecasting model shows promise for enhancing the operational efficiency and planning of community health centres in Rwanda. Based on this research protocol, recommendations will be made to integrate the model into routine operations for continuous improvement. Community Health Centres, Time-Series Forecasting, Healthcare Yield Improvement, Rwanda Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.

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