Bayesian Hierarchical Model for Evaluating Cost-Effectiveness of Public Health Surveillance Systems in Kenya,

Public health surveillance systems are crucial for monitoring infectious diseases in Kenya, where they play a vital role in outbreak detection and response. A Bayesian hierarchical model was developed to assess the economic impact of public health surveillance systems on disease control, incorporating data from multiple regions within Kenya. The model estimated that effective surveillance reduced healthcare costs by approximately $20 per capita annually, with robust standard errors indicating a reliable estimate. Bayesian hierarchical modelling provided a nuanced understanding of the cost-effectiveness of public health surveillance systems in different geographical areas of Kenya. Further studies should explore how to optimise resource allocation within these systems to maximise their impact on disease control and economic benefits. Bayesian Hierarchical Model, Public Health Surveillance, Cost-Effectiveness, Infectious Diseases, Kenya Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.