Public health surveillance systems are crucial for monitoring and responding to infectious diseases in Rwanda. A systematic literature review was conducted, including studies that used DID analysis to assess the impact of surveillance system improvements in Rwanda. The review aimed at identifying common methodologies and gaps in research methodology. The review identified one specific instance where a DID model showed an average efficiency gain of 25% post-intervention in monitoring measles outbreaks, with robust standard errors indicating significant confidence. This study highlights the effectiveness of using DID analysis for evaluating public health surveillance systems and underscores the need for consistent data collection methods to enhance system accuracy. Future research should focus on replicating these findings across other infectious diseases and incorporating qualitative feedback from healthcare workers to improve model validity. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.