Public health surveillance systems play a crucial role in monitoring disease outbreaks and ensuring effective public health responses in Senegal. A comprehensive search strategy was employed to identify relevant studies published between and . Studies were included if they utilised Bayesian hierarchical models to measure adoption rates in Senegalese public health surveillance systems. Bayesian hierarchical models demonstrated varying levels of adoption, with some reaching up to 75% across different healthcare facilities in rural areas. The use of Bayesian hierarchical models provided a robust framework for understanding and improving the adoption rates of public health surveillance systems in Senegal. Further research should focus on implementing these models in diverse settings to ensure their effectiveness and adaptability. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.