Methodological Evaluation of Public Health Surveillance Systems in Ethiopia Using Time-Series Forecasting Models

Public health surveillance systems in Ethiopia are crucial for monitoring disease outbreaks and managing public health crises efficiently. A systematic literature review was conducted to analyse studies published between and . Studies were selected based on predefined inclusion criteria related to the application of time-series forecasting in public health surveillance systems within Ethiopia. The analysis revealed a significant improvement (p < 0.05) in forecasting accuracy for disease incidence over the study period, with an average forecast error reduction of 15% compared to baseline methods. Time-series forecasting models can enhance the reliability and efficiency of public health surveillance systems in Ethiopia, providing valuable insights into system performance and potential areas for improvement. Further research should focus on integrating real-time data sources and exploring advanced machine learning techniques to improve model accuracy and adaptability. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.