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

ABSTRACT:

Background

Traditional influenza surveillance often suffers from reporting delays, hindering timely public health response. This study aimed to mitigate this limitation by developing an accurate deep learning framework for influenza prediction.

Methods

We constructed a 13-year (652-week) multi-source dataset for Yangzhou City, China, integrating influenza-like illness (ILI) surveillance data with Baidu search indices and meteorological variables. Six state-of-the-art deep learning models-GRU, Transformer, LSTM, TFT (Temporal Fusion Transformer), TCN (Temporal Convolutional Network), and N-BEATS-were systematically compared under 1-, 5-, and 9-week sliding time windows. Based on their robust performance across windows, GRU, TCN, and Transformer were selected as core base lear

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12802191 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature