Public health surveillance systems are crucial for monitoring infectious diseases in Rwanda, but their effectiveness varies. A methodological evaluation is necessary to assess these systems and identify areas for improvement. A difference-in-differences (DiD) approach was employed, utilising pre- and post-intervention data. The DiD model accounts for potential confounders through regression analysis to measure cost-effectiveness. The DiD model revealed a statistically significant reduction in disease incidence by 15% within the surveillance area compared to non-surveillance regions (p < 0.05). The DiD method provided robust evidence of the impact of public health surveillance systems on infectious diseases, demonstrating cost-effectiveness. Further research is recommended to validate these findings and explore scalability of the DiD model in different settings. public health surveillance, difference-in-differences, cost-effectiveness analysis, Rwanda Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.