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

作者：Qingqing Zhu, Minglei Zhu, Yuhang Cai, Junwei Xiang, Shuwen Li, Biao Zhu, Meng Zhu, Lei Gong, Sai Hou, J C He, Junling Yu, Jiabing Wu · 发表于：Frontiers in Public Health · 年份：2026 · DOI：10.3389/fpubh.2026.1806095 · 被引用次数：1 · 研究领域：Data-Driven Disease Surveillance、Influenza Virus Research Studies、COVID-19 epidemiological studies

Background Influenza 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. Methods Single 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 R 2 (explained variance), RMSE (root mean square error), MAE (mean absolute error), and MAPE (mean absolute percentage error). Results ARIMA exhibits poor fit for non-stationary sequences (training set R 2 = −3.66; test set R 2 = 0.03). Prophet effectively captures long-term trends (training/test set R 2 = 0.38/0.88). XGBoost shows overfitting (training/test set R 2 = 0.99/0.74). The Stacked-NNLS model demonstrated significantly superior robustness (training/test R 2 = 0.94/0.94), outperforming baseline models across all metrics. Conclusion By integrating statistical, seasonal, and nonlinear modeling approaches, Stacked-NNLS demonstrated robust predictive performance in capturing influenz...