African Rehabilitation Sciences | 27 June 2004
The Methodological Evaluation and Cost-Effectiveness of Public Health Surveillance Systems in Ghana Using Time-Series Forecasting Models
F, o, s, u, K, w, a, m, e, ,, K, o, f, i, O, k, u, d, z, e, t, o, ,, Y, a, w, A, s, a, r, e
Abstract
Public health surveillance systems are crucial for monitoring diseases in Ghana. However, their effectiveness can be improved through methodological evaluation and cost-effectiveness analysis. The study employed a time-series forecasting model (e.g., ARIMA) to analyse data from Ghana’s surveillance system. Uncertainty was quantified with robust standard errors and confidence intervals around forecasted values. A significant proportion (p < 0.05) of the variance in disease incidence could be explained by time-series forecasting models, indicating their predictive accuracy. The findings suggest that integrating advanced statistical models into public health surveillance systems can enhance their cost-effectiveness and reliability. Public health officials should consider implementing these methodologies to improve disease monitoring and 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.