Public health surveillance systems in Tanzania are critical for monitoring diseases and implementing effective interventions. However, their reliability and efficiency require rigorous evaluation. The study will employ a time-series forecasting model to analyse historical data from existing surveillance systems. The forecast accuracy will be evaluated using mean absolute error (MAE) with robust standard errors, providing a measure of reliability. A preliminary analysis suggests that the MAE for one system is within ±5% of its actual values in recent years, indicating consistent and reliable forecasting performance. The time-series forecasting model demonstrates the potential to assess the reliability of public health surveillance systems in Tanzania, with specific applications in disease outbreak prediction and intervention planning. Future studies should expand the evaluation to include more systems and incorporate real-time data integration for enhanced accuracy and timeliness. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.