Bayesian Hierarchical Model for Evaluating Cost-Effectiveness of Regional Monitoring Networks in Kenya,

The cost-effectiveness of regional monitoring networks in managing environmental and health issues is crucial for resource allocation in developing countries like Kenya. Previous studies have evaluated these networks but often lack a comprehensive methodological framework. A Bayesian hierarchical model was employed to analyse data on costs and benefits associated with regional monitoring systems. This approach allows for the integration of multiple levels of uncertainty and variability inherent in such complex systems. The analysis revealed that incorporating spatial and temporal dependencies improved cost-effectiveness estimates, indicating a significant improvement over traditional models by accounting for network structure and environmental heterogeneity. The Bayesian hierarchical model provides a robust framework for evaluating the cost-effectiveness of regional monitoring networks in Kenya's diverse geographical and socio-economic settings. This methodological approach can be applied to other regions, enhancing the evaluation of similar systems. Future work should explore scalability and potential improvements in the underlying statistical models. Bayesian hierarchical model, cost-effectiveness, regional monitoring networks, Kenya The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.