Public health surveillance systems in Senegal are crucial for monitoring diseases and managing outbreaks effectively. A quasi-experimental design was employed to compare pre- and post-intervention data, with statistical analysis using an Ordinary Least Squares (OLS) regression model for estimating costs and benefits. The analysis revealed that the system's cost-effectiveness varied by disease type, with respiratory infections showing a significant positive change in surveillance accuracy (p < 0.05). The quasi-experimental design provided insights into system performance but highlighted areas needing further refinement. Further research is recommended to validate these findings and explore potential cost-saving measures. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.