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Background <p>Accurate and timely early warning of seasonal influenza epidemics continues to pose a critical public health challenge. Innovative methodologies leveraging network data sources, such as internet search queries, serve as a valuable complement to traditional laboratory surveillance in terms of timeliness. This study aimed to develop and validate a deep-learning model for influenza prediction based on multi-source data.</p> Methods <p>Using etiological surveillance data of influenza viruses among influenza-like illness (ILI) cases in four megacities (Beijing, Tianjin, Shanghai, and Shenzhen) during 2013 and 2018, we developed prediction models based on weekly Baidu index data and meteorological indicators. A long short-term memory (LSTM) model was compared against three machine learning algorithms. The optimal model was used for weekly forecasting of influenza activity.</p> Results <p>The LSTM model exhibited superior performance (maximum R<sup>2</sup> : 0.80‒0.94 across cities) when compared to the three machine-learning models (maximum R<sup>2</sup>: 0.73‒0.82), and effectively predicted the weekly positive detection rate of influenza viruses with a lead time of 1‒3 weeks for the four megacities. Its accuracy was robustly maintained in medium-term rolling forecasts (14‒35 weeks) across all four megacities. Although the significance of predictors varied geographically, the Baidu index for “Tamiflu” was consistently a dominant predictor in three megacities.</p> Conclusions <p>This study validates the significant potential of integrating network big data and deep-learning algorithm for influenza surveillance. The developed LSTM model provides a streamlined and effective tool for the early detection and warning of seasonal influenza epidemics.</p>

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Forecasting influenza activity based on internet retrieval behavior and meteorological data in four megacities of China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月2日

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  • Chun-Xi Shan,
  • Yan-He Wang,
  • Qiang Xu,
  • Guo-Lin Wang,
  • Chen-Long Lv,
  • Li-Qun Fang

摘要

Background

Accurate and timely early warning of seasonal influenza epidemics continues to pose a critical public health challenge. Innovative methodologies leveraging network data sources, such as internet search queries, serve as a valuable complement to traditional laboratory surveillance in terms of timeliness. This study aimed to develop and validate a deep-learning model for influenza prediction based on multi-source data.

Methods

Using etiological surveillance data of influenza viruses among influenza-like illness (ILI) cases in four megacities (Beijing, Tianjin, Shanghai, and Shenzhen) during 2013 and 2018, we developed prediction models based on weekly Baidu index data and meteorological indicators. A long short-term memory (LSTM) model was compared against three machine learning algorithms. The optimal model was used for weekly forecasting of influenza activity.

Results

The LSTM model exhibited superior performance (maximum R2 : 0.80‒0.94 across cities) when compared to the three machine-learning models (maximum R2: 0.73‒0.82), and effectively predicted the weekly positive detection rate of influenza viruses with a lead time of 1‒3 weeks for the four megacities. Its accuracy was robustly maintained in medium-term rolling forecasts (14‒35 weeks) across all four megacities. Although the significance of predictors varied geographically, the Baidu index for “Tamiflu” was consistently a dominant predictor in three megacities.

Conclusions

This study validates the significant potential of integrating network big data and deep-learning algorithm for influenza surveillance. The developed LSTM model provides a streamlined and effective tool for the early detection and warning of seasonal influenza epidemics.