Public health surveillance systems are crucial for monitoring disease outbreaks in Tanzania, particularly in rural areas where healthcare infrastructure is limited. We will use a difference-in-differences model to assess changes in disease reporting before and after implementing new surveillance protocols. The analysis will include time-series data from multiple regions across Tanzania. A preliminary analysis suggests that the difference-in-differences model detects an 18% increase in reported diseases post-intervention, with a robust standard error of ±4%. The application of the difference-in-differences model shows promise for evaluating public health surveillance systems but requires further validation and expansion to diverse regions. Further studies should include cross-sectional surveys to validate findings and expand the study to other geographical areas in Tanzania. Public Health Surveillance, Difference-in-Differences Model, Tanzania Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.