Public health surveillance systems are essential for monitoring infectious diseases in developing countries like Ghana. Bayesian hierarchical models were applied to analyse surveillance data from -, accounting for spatial and temporal variations. The model identified regions with underreporting rates of up to 35% in disease incidence, necessitating targeted interventions. Bayesian hierarchical models provide a robust framework for assessing surveillance systems' performance and cost-effectiveness. Targeted interventions should be prioritised in areas with high underreporting rates identified by the model. Public health surveillance, Bayesian hierarchical models, Ghana, Cost-effectiveness Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.