African Immunology Journal (Core Life Science) | 17 August 2009
Time-Series Forecasting Model for Measuring Adoption Rates in District Hospital Systems in South Africa: A Methodological Evaluation
S, i, p, h, o, M, k, h, i, z, e, ,, M, p, h, o, M, a, t, h, e, b, u, l, a, ,, N, k, o, s, i, h, l, e, N, k, a, b, i, n, d, e, ,, D, u, m, i, l, e, Z, u, l, u
Abstract
District hospitals in South Africa are pivotal for healthcare delivery but face challenges in resource allocation and service adoption. A time-series analysis was conducted to forecast adoption rates using historical data from South African district hospitals. The Box-Jenkins methodology was employed, including ARIMA models for modelling the series. The forecasting model demonstrated an R-squared value of 0.85 and a confidence interval of ±3% for one-year-ahead predictions, indicating moderate accuracy in predicting service adoption rates. The time-series model showed promise in accurately forecasting service adoption rates in South African district hospitals, validating its use as a robust methodological tool. Further research should explore the scalability and generalizability of this model to other healthcare systems with varying contexts and resource constraints. District Hospitals, Time-Series Forecasting, Adoption Rates, ARIMA, Confidence Interval 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.