Public health surveillance systems are critical for monitoring disease outbreaks in Nigeria. However, their reliability is often questioned due to potential biases and inconsistencies. A difference-in-differences regression analysis was conducted on surveillance records from to , adjusting for potential confounders such as seasonal variations and regional differences. The DID model revealed a significant improvement in the accuracy of reported cases over time, with an estimated increase of 34% in reporting efficiency post-implementation of new protocols. The results suggest that public health surveillance systems have improved reliability through targeted interventions and data standardization efforts. Further research should focus on system sustainability and scalability to ensure consistent performance across different regions and time periods. Public Health Surveillance, Difference-in-Differences Model, System Reliability, Nigeria Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.