{ "background": "Public health surveillance systems are critical for disease control, yet rigorous, longitudinal evaluations of their cost-effectiveness in low-resource settings are scarce. Existing assessments often lack robust counterfactuals and longitudinal rigour, limiting evidence for resource allocation.", "purpose and objectives": "This study aims to develop and apply a novel longitudinal difference-in-differences (DiD) model to evaluate the cost-effectiveness of integrated public health surveillance systems, using Uganda as a case study. The primary objective is to quantify the causal impact of surveillance enhancements on key health outcomes relative to their economic cost.", "methodology": "A longitudinal study design was employed, analysing panel data from health facilities. The core econometric model is a two-way fixed effects DiD specification: $Y{it} = \\alpha + \\beta (Treatment{it}) + \\gammai + \\lambdat + \\epsilon{it}$, where $Y{it}$ is the outcome for facility $i$ in period $t$. Treatment assignment was staggered. Inference was based on cluster-robust standard errors at the district level. Cost data were integrated to calculate incremental cost-effectiveness ratios.", "findings": "The analysis indicates a statistically significant positive effect of enhanced surveillance on outbreak detection timeliness. Preliminary model estimates suggest a reduction in median detection delay by approximately 40% (95% CI: 32% to 48%) in treated districts compared to controls. Full cost-effectiveness results are pending finalisation of longitudinal cost data.", "conclusion": "The proposed longitudinal DiD framework provides a methodologically robust approach for causal inference in surveillance system evaluation. Initial findings support the effectiveness of system enhancements, though final cost-effectiveness conclusions await complete economic analysis.", "recommendations": "Health ministries should adopt longitudinal, counterfactual-based models for surveillance investment decisions. Future research should integrate real-time data streams and explore heterogeneity in treatment effects across different system components.", "key words": "cost-effectiveness analysis, difference-in-differences, health economics, longitudinal study, public health