Bayesian Hierarchical Model for Evaluating Public Health Surveillance Systems in Ghana,

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.