African Journal of Pharmacology (Core Science) | 04 August 2007
Time-Series Forecasting Model for Evaluating Public Health Surveillance Efficiency in Ethiopia: A Methodological Assessment
K, a, s, s, a, A, s, f, a, w, a, y, e, ,, A, r, e, g, a, w, i, M, e, n, g, i, s, t, i, e, ,, F, a, s, i, l, T, e, k, l, e, h, a, i, m, o, v, e
Abstract
Public health surveillance systems in Ethiopia are crucial for monitoring diseases and managing public health emergencies efficiently. A novel approach was developed using a time-series forecasting model (e.g., ARIMA) for analysing surveillance data. Robust standard errors were employed to account for uncertainty in the predictions. The model showed that public health interventions had a significant positive impact on disease prevalence, with a $ARIMA(1,0,1)$ forecast predicting a decrease of 25% in new cases over the next year. This study validates the effectiveness of time-series forecasting models for evaluating and improving public health surveillance systems in Ethiopia. Implementing regular model updates based on real-time data could further enhance the accuracy and predictive power of these systems.