A stacked ensemble model with NNLS-based weighting for influenza forecasting: a case study of Anhui Province, China

BackgroundInfluenza poses a significant global public health threat, with its pandemic potential and seasonal variability presenting formidable challenges to prediction accuracy. This study leverages high-quality weekly data (incidence rates, viral subtypes, and meteorological indicators) from the provincial influenza surveillance system in Anhui Province, eastern China, spanning 2015–2025. A multi-source data fusion model was developed to overcome the limitations of traditional methods in modeling nonlinear transmission dynamics and multi-factor synergistic effects.MethodsSingle models were constructed using ARIMA, Prophet, and XGBoost, then stacked into an interpretable ensemble model (Stacked-NNLS) using non-negative least squares (NNLS). Performance was comprehensively evaluated using R2 (explained variance), RMSE (root mean square error), MAE (mean absolute error), and MAPE (mean absolute percentage error).ResultsARIMA exhibits poor fit for non-stationary sequences (training set R2 = −3.66; test set R2 = 0.03). Prophet effectively captures long-term trends (training/test set R2 = 0.38/0.88). XGBoost shows overfitting (training/test set R2 = 0.99/0.74). The Stacked-NNLS model demonstrated significantly superior robustness (training/test R2 = 0.94/0.94), outperforming baseline models across all metrics.ConclusionBy integrating statistical, seasonal, and nonlinear modeling approaches, Stacked-NNLS demonstrated robust predictive performance in capturing influenza trends, seasonal fluctuations, and complex interactions among multiple factors, suggesting its potential utility for infectious disease early warning and public health decision-making.