Public health surveillance systems in Kenya are crucial for monitoring diseases such as malaria and tuberculosis. The study employed a difference-in-differences (DID) model to assess changes in disease incidence rates before and after the implementation period. A significant reduction of 15% in malaria cases was observed post-intervention, with a confidence interval indicating robustness of results. The DID approach successfully highlighted the impact of surveillance system enhancements on reducing disease incidence. Further research should explore scalability and cost-effectiveness of similar interventions across different regions. Public Health Surveillance, Difference-in-Differences Model, Risk Reduction, Kenya Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.