Public health surveillance systems in Senegal play a crucial role in monitoring infectious diseases such as malaria and cholera. A comprehensive search of academic databases including PubMed and Scopus was conducted. Studies published between and were included based on predefined inclusion criteria related to public health surveillance system evaluations in Senegal using quasi-experimental methods. Quasi-experimental designs employing difference-in-differences (ΔD) models have shown mixed results in measuring efficiency gains, with some studies reporting statistically significant improvements in detection rates for certain diseases. While ΔD models are widely used, their robustness and generalizability remain uncertain due to varying implementation contexts and data quality issues. Future research should focus on validating these methods using mixed-method approaches that combine quantitative and qualitative data. Enhanced data standardization is also recommended for improving accuracy in efficiency assessments. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.