African Veterinary Medicine Journal | 13 November 2010
Time-Series Forecasting Model for Evaluating Clinical Outcomes in Rural Clinics Systems in South Africa: A Methodological Assessment
M, p, h, o, L, e, k, o, t, a, ,, S, i, p, h, o, M, k, h, i, z, e
Abstract
Clinical outcomes in rural clinics in South Africa have been subject to variability due to resource limitations and logistical challenges. A time-series analysis was conducted using data from rural clinics. The model utilised autoregressive integrated moving average (ARIMA) methodology to forecast future trends based on historical data. The ARIMA model demonstrated a moderate level of accuracy in forecasting clinical outcomes, with an R² value of 0.65 and confidence intervals indicating the model's robustness across different time horizons. The findings suggest that the ARIMA model could be effectively employed to enhance decision-making processes within rural clinic systems. Further research is recommended to validate these results in diverse settings and to explore additional factors influencing clinical outcomes. Rural clinics, time-series forecasting, clinical outcomes, autoregressive integrated moving average (ARIMA), resource allocation Treatment effect was estimated with $\text{logit}(p<em>i)=\beta</em>0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.