Public health surveillance systems are crucial for monitoring diseases and guiding public health interventions in Uganda. A mixed-method approach combining quantitative data from surveys and qualitative interviews was employed to assess system performance. Statistical models were used to estimate costs and benefits. The analysis revealed that the current surveillance system had an average cost of $50 per case detected, with a precision interval (CI) of ±10% indicating moderate variability in cost estimates. Quasi-experimental designs offer robust methods for evaluating public health systems and can be used to inform policy decisions on resource allocation. Investment should focus on improving data collection infrastructure, enhancing training programmes for surveillance personnel, and expanding coverage in rural areas where resources are scarce. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.