Public health surveillance is critical for food security and disease control, yet robust methodological frameworks for evaluating its cost-effectiveness in resource-limited settings are lacking. This case study aimed to develop and apply a novel quasi-experimental methodology to evaluate the cost-effectiveness of enhanced surveillance systems, comparing their impact on outbreak detection timeliness and resource utilisation. A difference-in-differences model was employed, using panel data from surveillance units. The core specification was $Y_{it} = \beta_0 + \beta_1 \text{Treated}_{i} + \beta_2 \text{Post}_{t} + \delta (\text{Treated}_{i} \times \text{Post}_{t}) + \epsilon_{it}$, where $Y_{it}$ is the detection time. Inference was based on cluster-robust standard errors. Cost data were integrated for incremental cost-effectiveness ratios. The intervention was associated with a statistically significant reduction in mean detection time of 4.2 days (95% CI: 2.1, 6.3). The incremental cost-effectiveness ratio was estimated at $\unicode{x00A3}1,850$ per outbreak detected one week earlier, with sensitivity analyses confirming robustness. The applied econometric model provides a rigorous framework for attributing changes in surveillance outcomes, demonstrating that the enhanced system was cost-effective under local conditions. Programme planners should adopt quasi-experimental designs for surveillance evaluation. Investment should prioritise integrated data platforms that reduce reporting delays, as modelled here. health surveillance, cost-effectiveness analysis, difference-in-differences, quasi-experimental design, programme evaluation This study provides a novel application of a difference-in-differences framework to attribute changes in surveillance performance and calculate cost-effectiveness, generating a replicable model for similar settings.