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International Symposium on Bioinformatics Research and Applications

ISBRA 2018: Bioinformatics Research and Applications pp 259–264Cite as

LSTM Recurrent Neural Networks for Influenza Trends Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10847))

Abstract

Influenza-like illness (ILI) is an acute respiratory infection causes substantial mortality and morbidity. Predict Influenza trends and response to a health disease rapidly is crucial to diminish the loss of life. In this paper, we employ the long short term memory (LSTM) recurrent neural networks to forecast the influenza trends. We are the first one to use multiple and novel data sources including virologic surveillance, influenza geographic spread, Google trends, climate and air pollution to predict influenza trends. Moreover, We find there are several environmental and climatic factors have the significant correlation with ILI rate.

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Author information

Authors and Affiliations

  1. Kennesaw State University, Kennesaw, GA, USA

    Liyuan Liu, Meng Han, Yiyun Zhou & Yan Wang

Authors
  1. Liyuan Liu
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  2. Meng Han
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  3. Yiyun Zhou
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  4. Yan Wang
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Corresponding author

Correspondence to Meng Han .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Fa Zhang

  2. Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

  3. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  4. Chinese Academy of Sciences, Beijing, China

    Shihua Zhang

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Cite this paper

Liu, L., Han, M., Zhou, Y., Wang, Y. (2018). LSTM Recurrent Neural Networks for Influenza Trends Prediction. In: Zhang, F., Cai, Z., Skums, P., Zhang, S. (eds) Bioinformatics Research and Applications. ISBRA 2018. Lecture Notes in Computer Science(), vol 10847. Springer, Cham. https://doi.org/10.1007/978-3-319-94968-0_25

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  • DOI: https://doi.org/10.1007/978-3-319-94968-0_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94967-3

  • Online ISBN: 978-3-319-94968-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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