Public health surveillance systems in Senegal have been established to monitor infectious diseases, but their reliability over time has not been systematically evaluated. A time-series forecasting model will be developed using historical data from the Senegalese Ministry of Health. Model parameters will be estimated, and uncertainty around these estimates will be quantified with robust standard errors. The forecasted trends showed a consistent increase in notifiable diseases over the study period, indicating that the surveillance system was effective in detecting such outbreaks. This study demonstrates the utility of time-series forecasting models for evaluating public health surveillance systems and provides insights into their reliability over time. Public health officials should consider implementing or refining existing surveillance systems based on this evaluation to enhance early detection capabilities. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.