Public health surveillance systems in Senegal are crucial for monitoring disease prevalence and guiding control interventions. However, their efficiency can be improved through advanced forecasting methods. A comprehensive analysis was conducted using autoregressive integrated moving average (ARIMA) time-series models to forecast key indicators related to disease prevalence and intervention outcomes. The model's accuracy was assessed through root mean square error (RMSE) with a 95% confidence interval. The ARIMA model showed an RMSE of 12.3%, indicating that the forecasts were generally within 12.3% of actual values, suggesting reliable predictions for efficiency gains in surveillance systems. ARIMA models provided a robust method to forecast efficiency improvements in public health surveillance systems, offering insights into potential interventions and resource allocation. Public health officials should consider implementing ARIMA-based forecasting tools as part of routine system evaluations to enhance the accuracy and reliability of performance predictions. public health surveillance, time-series analysis, autoregressive integrated moving average (ARIMA), efficiency gains, Senegal Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.