Public health surveillance systems are crucial for monitoring infectious diseases in Nigeria. However, their cost-effectiveness varies significantly across different regions and healthcare settings. A Bayesian hierarchical model was developed to assess the cost-effectiveness of public health surveillance systems in Nigeria. The model accounts for variability across different regions and incorporates uncertainty through robust standard errors. The analysis revealed that certain regions benefited more from surveillance efforts, with a higher proportion (25%) demonstrating significant cost savings compared to others. The Bayesian hierarchical model provided insights into the optimal allocation of resources for enhanced surveillance effectiveness in Nigeria. Public health authorities should prioritise investments in regions where surveillance can yield substantial cost savings and improve outbreak response times. Bayesian Hierarchical Model, Public Health Surveillance, Cost-Effectiveness Analysis, Nigeria Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.