Forecasting System Reliability in Ghanaian Public Health Surveillance: A Time-Series Analysis

Public health surveillance systems in Ghana are crucial for monitoring disease trends and resource allocation. A time-series analysis was conducted using an ARIMA (AutoRegressive Integrated Moving Average) model with robust standard errors estimated at 95% confidence intervals. The forecasted reliability index showed a mean improvement of 20% in data accuracy over the previous year’s system performance, indicating effective forecasting mechanisms. The ARIMA model successfully predicted future public health surveillance outcomes with reasonable uncertainty bounds. Implementing continuous monitoring and updating of the ARIMA model will ensure ongoing reliability in Ghanaian public health systems. Ghana, Public Health Surveillance, Time-Series Analysis, Forecasting System Reliability Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.