Public health surveillance systems in Nigeria are crucial for monitoring infectious diseases and ensuring timely interventions. However, their effectiveness can be improved by assessing risk reduction strategies. A Bayesian hierarchical model was employed to analyse surveillance data from multiple regions. The model accounts for spatial and temporal variations, providing a nuanced understanding of disease patterns and intervention impacts. The model indicated that targeted vaccination campaigns significantly reduced the incidence of measles by approximately 50% in monitored areas over one year. Bayesian hierarchical models offer a robust method for assessing risk reduction in public health surveillance systems, enhancing their efficiency and effectiveness. Public health authorities should prioritise implementation of these models to improve disease control and resource allocation. Bayesian Hierarchical Model, Public Health Surveillance, Nigeria, Measles Reduction Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.