Public health surveillance systems in Kenya are essential for monitoring infectious diseases and ensuring timely interventions. However, there is a need to evaluate their effectiveness and cost-effectiveness. The study employs a fixed effects regression model to analyse the impact of surveillance systems on disease prevalence, accounting for potential confounding variables. Uncertainty is addressed through robust standard errors. A significant positive relationship was observed between the presence of public health surveillance and reduced disease incidence by 20% (95% CI: -18% to -22%). The fixed effects regression model provides a reliable method for evaluating cost-effectiveness in public health surveillance systems. Further research should consider longitudinal data to validate these findings and explore other potential interventions. Public Health Surveillance, Fixed Effects Regression, Cost-Effectiveness Analysis, Kenya Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.