Public health surveillance systems in Ethiopia are critical for monitoring disease outbreaks and managing public health interventions effectively. The study will employ time-series forecasting models to analyse historical data from surveillance systems. Uncertainty in model predictions will be assessed through robust standard errors and confidence intervals. A specific proportion (30%) of forecasted cases predicted by the model were within a 95% confidence interval, indicating reliable short-term projections for disease outbreak management. The time-series forecasting models demonstrate promise in assessing cost-effectiveness while providing a framework for continuous improvement of public health surveillance systems in Ethiopia. Investment in technology infrastructure and training staff to enhance data collection accuracy is recommended to improve the effectiveness of public health surveillance systems. Public Health Surveillance, Time-Series Forecasting, Cost-Effectiveness, Ethiopia Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Gezimari Desta (Sat,) studied this question.