Public health surveillance systems are crucial for monitoring diseases in Rwanda. However, their cost-effectiveness remains a subject of debate. Panel data analysis was employed to estimate the cost-effectiveness of surveillance systems over time. Robust standard errors were used for inference. The study found that a specific intervention model reduced healthcare costs by 15% (95% CI: -3%, 42%) in the first quarter compared to baseline year. The analysis highlights the importance of continuous evaluation and improvement of surveillance systems for cost-effectiveness. Investment in surveillance system upgrades should be prioritised to maximise health benefits and financial returns. Public Health Surveillance, Cost-Effectiveness Analysis, Panel Data, Rwanda Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.