ArticleBMC infectious diseases2026
Forecasting influenza activity based on internet retrieval behavior and meteorological data in four megacities of China.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
backgroundAccurate 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.
methodsUsing 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.
resultsThe LSTM model exhibited superior performance (maximum R
conclusionsThis 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.
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