Public health surveillance systems are critical for monitoring and responding to infectious diseases in resource-limited settings like Kenya. A Bayesian hierarchical model was developed to analyse data from Kenyan public health surveillance systems. This approach accounts for variability at different levels and incorporates prior knowledge to estimate costs and benefits. The analysis revealed that the cost-effectiveness of surveillance systems varied significantly by region, with some areas requiring more investment per case identified compared to others. The Bayesian hierarchical model provided insights into where resources should be allocated most effectively within Kenya's public health surveillance infrastructure. Public health officials are advised to use the findings from this study to tailor surveillance strategies and allocate funds based on regional cost-effectiveness estimates. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Kinyanjui et al. (Sat,) studied this question.
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