Time-Series Forecasting Model for Evaluating Adoption Rates in Community Health Centres in Uganda: A Methodological Assessment

Community health centres in Uganda face challenges related to resource allocation and patient adoption rates. A comprehensive methodological approach was employed, including data collection from existing records, application of ARIMA (AutoRegressive Integrated Moving Average) model for forecasting future trends, and sensitivity analysis to assess model robustness. The ARIMA model demonstrated a moderate R² value ($R^2 = 0.75$), indicating that the model explains approximately 75% of the variance in adoption rates over time. ARIMA models provide valuable insights for predicting future trends and can be utilised to inform strategic planning within community health centres, though further validation is recommended. Recommendation for policymakers includes prioritising investments in areas with lower model predictions and implementing targeted interventions based on forecasted adoption rates.