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Neural network method and multiscale modeling of the COVID-19 epidemic in Korea

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Abstract

A multiscale modeling procedure is proposed with integrating dynamical models and small-world network models to describe the transmission of COVID-19 in Korea, which featured many infections due to aggregation. Two types of dynamical models are founded on a national scale to describe the spreading patterns of the disease and the intervention measures. A small-world network is established on a local scale to illustrate the five serious aggregated infection events. Furthermore, a physics-informed neural network algorithm is employed to solve the dynamical models, incorporating a small-world network random contacting evolution, the numerical simulation results demonstrate the effectiveness of the proposed method.

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Data Availability Statement

This manuscript has associated data in a data repository. [Authors’ comment: These data were derive from the following resources available in the public domain: (Korea Disease Control and Prevention Agency)https://www.kdca.go.kr/index.es?sid=a3.]

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Grant No. 11901234), Natural Science Foundation of Jilin Province (Grant Nos. 20210101481JC and 20210101482JC), Shanghai Municipal Science and Technology Major Project (Grant No. 2021SHZDZX0103), Natural Science Foundation-Division of Mathematical Sciences (Grant No. 2208499).

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Authors and Affiliations

  1. School of Mathematics, Jilin University, Changchun, 130012, Jilin, China

    Ziqian Li, Jiwei Jia & Guidong Liao

  2. National Applied Mathematical Center (Jilin), Changchun, 130012, Jilin, China

    Jiwei Jia

  3. Department of Mathematics, Texas State University, San Marcos, Texas, 78666, USA

    Young Ju Lee

  4. School of Public Health, Jilin University, Changchun, 130021, Jilin, China

    Siyu Liu

  5. State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Jilin University, Changchun, 130062, Jilin, China

    Siyu Liu

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  1. Ziqian Li
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  2. Jiwei Jia
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Correspondence to Siyu Liu.

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Li, Z., Jia, J., Liao, G. et al. Neural network method and multiscale modeling of the COVID-19 epidemic in Korea. Eur. Phys. J. Plus 138, 752 (2023). https://doi.org/10.1140/epjp/s13360-023-04373-8

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