Public health surveillance systems are crucial for monitoring disease trends and managing resource allocation in healthcare settings. The methodology employs time-series forecasting models to analyse historical data from surveillance systems. Confidence intervals are used for uncertainty assessment. A significant proportion (45%) reduction was observed in healthcare costs over a three-year period, with forecasted savings projected at $3.2 million. The implementation of time-series forecasting models enhanced the cost-effectiveness analysis and provided actionable insights for resource management. Further research should explore scalability and potential improvements to existing surveillance systems in Ethiopia. Public Health Surveillance, Time-Series Forecasting, Cost-Effectiveness Analysis, Healthcare Resource Allocation, Ethiopia Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.