Public health surveillance systems in Nigeria are crucial for monitoring diseases and outbreaks but face challenges in data collection and analysis. A multilevel regression model was employed to analyse surveillance system performance across different levels (central, state, local). The model accounts for hierarchical data structures and uncertainty in parameter estimates using robust standard errors. Multilevel analysis revealed a significant improvement in disease detection rates at the local level compared to central and state levels, with a confidence interval of [0.5, 1.2]. The multilevel regression model highlights the need for targeted interventions to enhance surveillance capabilities at all levels. Investment in infrastructure and training is recommended to improve data collection accuracy and timely response mechanisms in Nigeria's public health surveillance systems. Public Health Surveillance, Multilevel Regression Analysis, Disease Detection, Nigeria Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.