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STE-COVIDNet: A Multi-channel Model with Attention Mechanism for Time Series Prediction of COVID-19 Infection

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Intelligent Computing Theories and Application (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13394))

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Abstract

The outbreak of COVID-19 has had a significant impact on the world. The prediction of COVID-19 can conduct the distribution of medical supplies and prevent further transmission. However, the spread of COVID-19 is affected by various factors, so the prediction results of previous studies are limited in practical application. A deep learning model with multi-channel combined multiple factors including space, time, and environment (STE-COVIDNet) is proposed to predict COVID-19 infection accurately in this paper. The attention mechanism is applied to score each environment to reflect its impact on the spread of COVID-19 and obtain environmental features. The experiments on the data of 48 states in the United States prove that STE-COVIDNet is superior to other advanced prediction models in performance. In addition, we analyze the attention weights of the environment of the 48 states obtained by STE-COVIDNet. It is found that the same environmental factors have inconsistent effects on COVID-19 transmission in different regions and times, which explains why researchers have varying results when studying the impact of environmental factors on transmission of COVID-19 based on data from different areas. STE-COVIDNet has a certain explainability and can adapt to the environmental changes, which ultimately improves our predictive performance.

This work was supported by the National Nature Science Foundation of China under grant 61873156.

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Hongjian He, Xinwei Lu, Dingkai Huang & Jiang Xie

Authors
  1. Hongjian He
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  2. Xinwei Lu
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  3. Dingkai Huang
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  4. Jiang Xie
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Corresponding author

Correspondence to Jiang Xie .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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He, H., Lu, X., Huang, D., Xie, J. (2022). STE-COVIDNet: A Multi-channel Model with Attention Mechanism for Time Series Prediction of COVID-19 Infection. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_70

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  • DOI: https://doi.org/10.1007/978-3-031-13829-4_70

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13828-7

  • Online ISBN: 978-3-031-13829-4

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