An Interpretable Decomposition-Based Framework for U.S. Influenza-like Illness Surveillance

Public-health respiratory-disease surveillance requires distinguishing expected seasonal illness activity from atypical changes that may warrant further investigation. Weekly influenza-like illness (ILI) data support situational monitoring, retrospective anomaly screening, and short-term forecasting, but these tasks are often conducted using separate statistical baselines. We empirically evaluated whether seasonal–trend decomposition using LOESS (STL) could provide a common interpretable baseline using weekly national U.S. ILI data from 1997 to 2025. Expected activity was represented by the estimated trend plus recurring seasonal component, while residuals represented baseline-adjusted deviations. Residual exceedance and persistence summaries were examined retrospectively, and forecast performance was evaluated through rolling-origin comparisons with SARIMA, seasonal naive, and ETS benchmarks at 4-, 8-, and 12-week horizons. The decomposition highlighted major departures from the recurring annual pattern, including the 2009 H1N1 pandemic and COVID-19-era disruptions. At the 4-week horizon, STL had the lowest RMSE (0.925), slightly below SARIMA (0.929), seasonal naive (1.270), and ETS (1.710). At the 8- and 12-week horizons, seasonal naive performed best, with RMSEs of 1.270 and 1.280, compared with 1.280 and 1.450 for STL. The two-week persistence rule produced no pre-peak warnings at any tested threshold, indicating that retrospective residual screening did not by itself provide effective early warning. The empirical study demonstrates that STL provides a transparent common baseline for linking monitoring, retrospective anomaly screening, and short-term forecasting while distinguishing expected seasonal progression from baseline-adjusted deviations.