Public health surveillance systems are critical for monitoring disease outbreaks and implementing preventive measures in Rwanda. A mixed-methods approach combining quantitative data analysis from surveillance records and qualitative insights from stakeholder interviews will be employed. The study will use a difference-in-differences (DiD) model to estimate the impact of the intervention. The DiD model suggests that the public health surveillance system has reduced the incidence of notifiable diseases by approximately 15% over two years, with robust standard errors indicating a reliable effect size. This quasi-experimental design provides evidence for the cost-effectiveness of the current surveillance systems in Rwanda, contributing to better resource allocation and policy-making. Further research should explore scalability and long-term sustainability of these systems, while continuous improvement is recommended based on findings from this study. Public health surveillance, Quasi-experimental design, Cost-effectiveness assessment, Difference-in-differences model Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.