African Ceramics Research (Applied Science/Tech) | 24 May 2007
Bayesian Hierarchical Model for Evaluating Public Health Surveillance Systems in Ghana,
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Abstract
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 $\text{logit}(p<em>i)=\beta</em>0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.