Public health surveillance systems in Senegal are essential for monitoring infectious diseases to inform timely interventions and resource allocation. A time-series forecasting model was developed using ARIMA methodology. The model's effectiveness and cost-effectiveness were assessed through sensitivity analysis considering uncertainty in input parameters. The ARIMA model accurately predicted ILI trends, with an R² value of 0.85 indicating a strong fit to the data. Sensitivity analyses showed that variations in ILI case reporting rates could affect model predictions by up to ±10%. The time-series forecasting model demonstrated robust performance and cost-effectiveness for public health surveillance, particularly in contexts with variable data quality. Further validation of the model across different disease types and geographic regions is recommended. Public Health Surveillance, Time-Series Forecasting, ARIMA Model, Senegal, Influenza-Like Illness Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.