Public health surveillance systems in Kenya are crucial for monitoring infectious diseases and other health threats. Bayesian hierarchical models offer a powerful tool to analyse these systems, enabling more accurate predictions and resource allocation. The methodology reviews existing literature on Bayesian hierarchical models applied to public health surveillance data from Kenya. It evaluates model performance and applicability across different scales (e. g. , district vs national level). The analysis reveals that Bayesian models enhance predictive accuracy by integrating spatial and temporal variability, leading to more reliable outbreak detection. Bayesian hierarchical models demonstrate significant potential for improving public health surveillance in Kenya, particularly in enhancing the timeliness and precision of disease tracking. Health authorities should prioritise model validation and implementation strategies that incorporate local data sources and expert feedback. Bayesian Hierarchical Models, Public Health Surveillance, Efficiency Gains, Infectious Diseases, Kenya Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Ochieng et al. (Sat,) studied this question.