Public health surveillance systems are crucial for monitoring infectious diseases in Rwanda. However, their effectiveness can be assessed through methodological evaluations. The study employed DiD models to analyse pre- and post-intervention data, focusing on the impact of enhanced surveillance protocols on disease incidence rates. Uncertainty was quantified using robust standard errors. A significant decrease in measles cases (30%) was observed after implementation of new surveillance measures, supported by a Y = β₀ + β₁Treated + β₂PreTreatment + β₃ (Treated * PreTreatment) + ε model where β₃= -0. 45 with 95% confidence interval (-0. 68 to -0. 22). The DiD models effectively demonstrated the impact of surveillance system improvements on disease reduction, providing a robust method for future public health interventions. Further research should explore scalability and cost-effectiveness of these methods in other Rwandan regions and diseases. Public Health Surveillance, Difference-in-Differences, Risk Reduction, DiD Models, Rwanda
Muhire et al. (Wed,) studied this question.