https://doi.org/10.1140/epjp/s13360-023-04373-8
Regular Article
Neural network method and multiscale modeling of the COVID-19 epidemic in Korea
1
School of Mathematics, Jilin University, 130012, Changchun, Jilin, China
2
National Applied Mathematical Center (Jilin), 130012, Changchun, Jilin, China
3
Department of Mathematics, Texas State University, 78666, San Marcos, Texas, USA
4
School of Public Health, Jilin University, 130021, Changchun, Jilin, China
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, 130062, Changchun, Jilin, China
Received:
1
August
2023
Accepted:
11
August
2023
Published online:
25
August
2023
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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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
