Theoretical frameworks for evaluating cost-effectiveness in regional monitoring networks are essential for resource allocation in agriculture. This article focuses on Kenya's context and aims to provide a robust theoretical foundation for assessing such systems. The methodology involves developing a Bayesian hierarchical model that accounts for spatial and temporal variability in data. This model will be used to estimate costs and benefits associated with different network configurations. The Bayesian hierarchical model provides a rigorous approach for assessing cost-effectiveness in regional monitoring networks, offering insights into resource allocation strategies for agricultural surveillance in Kenya. Based on the theoretical framework, recommendations include prioritising high-risk areas with dense network coverage while maintaining economic feasibility through efficient data collection and analysis methods. The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.
Smith et al. (Sun,) studied this question.