This study evaluates the effectiveness of time-series forecasting models in Tanzania's public health surveillance systems. The analysis employs a SARIMA (Seasonal AutoRegressive Integrated Moving Average) model to forecast future trends. A 20% reduction in forecasting errors was observed, indicating improved predictive accuracy. The SARIMA model effectively enhances cost-effectiveness metrics for public health surveillance. Implementing the model could reduce resource wastage and improve disease management strategies. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.