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SEIARN: Intelligent Early Warning Model of Epidemic Spread Based on LSTM Trajectory Prediction

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  • Liya Wang

    (College of Science, North China University of Science and Technology, Tangshan 063210, China
    Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China
    Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China
    The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China)

  • Yaxun Dai

    (College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China)

  • Renzhuo Wang

    (College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China)

  • Yuwen Sun

    (College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China)

  • Chunying Zhang

    (College of Science, North China University of Science and Technology, Tangshan 063210, China
    Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China
    Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China
    The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China)

  • Zhiwei Yang

    (College of Science, North China University of Science and Technology, Tangshan 063210, China)

  • Yuqing Sun

    (College of Economics, North China University of Science and Technology, Tangshan 063210, China)

Abstract

A SEIARN compartment model with the asymptomatic infection and secondary infection is proposed to predict the trend of COVID-19 more accurately. The model is extended according to the propagation characteristics of the novel coronavirus, the concepts of the asymptomatic infected compartment and secondary infection are introduced, and the contact rate parameters of the improved model are updated in real time by using the LSTM trajectory, in order to make accurate predictions. This SEIARN model first builds on the traditional SEIR compartment model, taking into account the asymptomatic infection compartment and secondary infection. Secondly, it considers the disorder of the trajectory and uses the improved LSTM model to predict the future trajectory of the current patients and cross-track with the susceptible patients to obtain the contact rate. Then, we conduct real-time updating of exposure rates in the SEIARN model and simulation of epidemic trends in Tianjin, Xi’an, and Shijiazhuang. Finally, the comparison experiments show that the SEIARN model performs better in prediction accuracy, MSE, and RMSE.

Suggested Citation

  • Liya Wang & Yaxun Dai & Renzhuo Wang & Yuwen Sun & Chunying Zhang & Zhiwei Yang & Yuqing Sun, 2022. "SEIARN: Intelligent Early Warning Model of Epidemic Spread Based on LSTM Trajectory Prediction," Mathematics, MDPI, vol. 10(17), pages 1-23, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3046-:d:896146
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    References listed on IDEAS

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    1. Cheng, Xinxin & Wang, Yi & Huang, Gang, 2021. "Global dynamics of a network-based SIQS epidemic model with nonmonotone incidence rate," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    2. Liyan Zhang & Jingfeng Guo & Jiazheng Wang & Jing Wang & Shanshan Li & Chunying Zhang, 2022. "Hypergraph and Uncertain Hypergraph Representation Learning Theory and Methods," Mathematics, MDPI, vol. 10(11), pages 1-22, June.
    3. Manuel De la Sen & Asier Ibeas & Raul Nistal & Ya Jia, 2021. "About Partial Reachability Issues in an SEIR Epidemic Model and Related Infectious Disease Tracking in Finite Time under Vaccination and Treatment Controls," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-21, March.
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    Cited by:

    1. Jiraporn Lamwong & Puntani Pongsumpun & I-Ming Tang & Napasool Wongvanich, 2022. "Vaccination’s Role in Combating the Omicron Variant Outbreak in Thailand: An Optimal Control Approach," Mathematics, MDPI, vol. 10(20), pages 1-29, October.

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