Public health surveillance systems in Senegal are essential for monitoring infectious diseases such as malaria and tuberculosis. Current cost-effectiveness evaluations often rely on traditional statistical methods that may not fully capture the complexities of system performance. A Bayesian hierarchical model will be employed to analyse cost and effectiveness metrics. This model will incorporate uncertainty through robust standard errors and confidence intervals, providing a comprehensive assessment of surveillance system performance across different regions in Senegal. The model indicates that the surveillance systems are moderately cost-effective, with an estimated cost-effectiveness ratio (CE) of 50 per quality-adjusted life year (QALY). This study demonstrates the utility of Bayesian hierarchical models for evaluating public health surveillance systems in diverse settings. The findings suggest that resource allocation strategies should be adjusted to optimise cost-effectiveness, particularly in areas with higher disease burden and lower healthcare infrastructure. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Gueye et al. (Fri,) studied this question.