Dynamic ensemble deep learning with multi-source data for robust influenza forecasting in Yangzhou

This study presents a multi-source deep learning framework that effectively integrates heterogeneous data to compensate for surveillance delays. Key contributions include: (i) a systematic sliding-window comparison that reveals the temporal strengths of different architectures; and (ii) a DWE + SRA ensemble strategy that dynamically adjusts model weights and corrects seasonal biases to substantially improve prediction stability. This work provides a scalable, data-driven paradigm for localized influenza forecasting and early warning.