Bayesian Hierarchical Model for Evaluating Risk Reduction in Public Health Surveillance Systems in Senegal

Public health surveillance systems in Senegal are essential for monitoring infectious diseases. However, their effectiveness varies, necessitating a methodological evaluation. A Bayesian hierarchical model was developed to assess the impact of interventions on disease incidence. The model accounts for spatial and temporal variability, using data from multiple regions across Senegal. The model estimated a 20% reduction in reported infectious diseases compared to baseline levels, with significant heterogeneity observed between surveillance sites. This study provides evidence of risk reduction through the use of a Bayesian hierarchical model tailored for public health surveillance systems in heterogeneous settings. Policy-makers should consider implementing similar models and scaling up interventions based on the identified risk reduction patterns. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.