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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.]

References

  1. N. Zhu, D. Zhang, W. Wang, X. Li, B. Yang, J. Song, X. Zhao, B. Huang, W. Shi, R. Lu et al., A novel coronavirus from patients with pneumonia in China, 2019. New Engl. J. Med. (2020)

  2. M. Chang, J. Baek, D. Park, Lessons from South Korea regarding the early stage of the COVID-19 outbreak. Healthcare 8, 229 (2020)

    Article  Google Scholar 

  3. J. Jia, J. Ding, S. Liu, G. Liao, J. Li, B. Duan, G. Wang, R. Zhang, Modeling the control of COVID-19: Impact of policy interventions and meteorological factors. Electronic J.Differ. Eqn. 23, 1–24 (2020)

    MATH  Google Scholar 

  4. S. Zhao, Q. Lin, J. Ran, S. Musa, G. Yang, W. Wang, Y. Lou, D. Gao, L. Yang, D. He et al., Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. Int. J. Infect. Dis. 92, 214–217 (2020)

  5. A. Kucharski, T. Russell, C. Diamond, Y. Liu, J. Edmunds, S. Funk, R. Eggo, F. Sun, M. Jit, J. Munday e al., Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect. Dis. 20, 553–558 (2020)

  6. J. Jia, S. Liu, Y. Liu, R. Shan, K. Zennir, R. Zhang, Modeling and reviewing analysis of the COVID-19 epidemic in algeria with diagnostic shadow. CSIAM Trans. Appl. Math. 3, 792–809 (2022)

    Article  MathSciNet  Google Scholar 

  7. J. Jia, S. Liu, J. Ding, G. Liao, L. Zhang, R. Zhang, The impact of multilateral imported cases of COVID-19 on the epidemic control in China. Commun. Math. Res. 36, 320–335 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  8. Y. Liu, A. Gayle, A. Wilder-Smith, J. Rocklv, The reproductive number of COVID-19 is higher compared to SARS coronavirus. J. Travel Med. (2020)

  9. T. Yao, J. Qian, W. Zhu, Y. Wang, G. Wang, A systematic review of lopinavir therapy for SARS coronavirus and MERS coronavirus possible reference for coronavirus disease-19 treatment option. J. Med. Virol. 92, 556–563 (2020)

    Article  Google Scholar 

  10. A. Yang, J. Liu, W. Tao, H. Li, The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients. Int. Immunopharm. 84, 106504 (2020)

    Article  Google Scholar 

  11. H. Kim, H. Hong, S. Yoon, Diagnostic performance of CT and reverse transcriptase-polymerase chain reaction for coronavirus disease 2019: a meta-analysis. Radiology (2020)

  12. A. Elmezayen, A. Al-Obaidi, A. ahin, K. Yeleki, Drug repurposing for coronavirus (COVID-19): in silico screening of known drugs against coronavirus 3CL hydrolase and protease enzymes. J. Biomol. Struct. Dyn. 39, 2980–2992 (2021)

    Article  Google Scholar 

  13. R. Yan, Y. Zhang, Y. Li, L. Xia, Y. Guo, Q. Zhou, Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science 367, 1444–1448 (2020)

    Article  ADS  Google Scholar 

  14. C. Wang, W. Li, D. Drabek, N. Okba, R. Haperen, A. Osterhaus, F. Kuppeveld, B. Haagmans, F. Grosveld, B. Bosch, A human monoclonal antibody blocking SARS-CoV-2 infection. Nat. Commun. 11, 1–6 (2020)

    Google Scholar 

  15. S. Moghadas, A. Shoukat, M. Fitzpatrick, C. Wells, P. Sah, A. Pandey, J. Sachs, Z. Wang, L. Meyers, B. Singer et al., Projecting hospital utilization during the COVID-19 outbreaks in the United States. Proc. Nat. Acad. Sci. 117, 9122–9126 (2020)

  16. J. Rocklv, H. Sjdin, A. Wilder-Smith, COVID-19 outbreak on the Diamond Princess cruise ship: estimating the epidemic potential and effectiveness of public health countermeasures. J. Travel Med. 27, taaa030 (2020)

    Article  Google Scholar 

  17. M. Hashim, A. Alsuwaidi, G. Khan, Population risk factors for COVID-19 mortality in 93 countries. J. Epidemiol. Global Health 10, 204 (2020)

    Article  Google Scholar 

  18. Korea Disease Control and Prevention Agency. https://www.kdca.go.kr/index.es?sid=a3

  19. A. Amiri Mehra, M. Shafieirad, Z. Abbasi, I. Zamani, Parameter estimation and prediction of COVID-19 epidemic turning point and ending time of a case study on SIR/SQAIR epidemic models. Comput. Math. Methods Med. (2020)

  20. C. Kwuimy, F. Nazari, X. Jiao, P. Rohani, C. Nataraj, Nonlinear dynamic analysis of an epidemiological model for COVID-19 including public behavior and government action. Nonlinear Dyn. 101, 1545–1559 (2020)

    Article  MATH  Google Scholar 

  21. S. Kim, Y. Seo, E. Jung, Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes in Korea. Epidemiol. Health 42 (2020)

  22. K. Min, S. Tak, Dynamics of the COVID-19 epidemic in the post-vaccination period in Korea: a rapid assessment. Epidemiol. Health 43 (2021)

  23. E. Kharazmi, M. Cai, X. Zheng, Z. Zhang, G. Lin, G. Karniadakis, Identifiability and predictability of integer-and fractional-order epidemiological models using physics-informed neural networks. Nat. Comput. Sci. 1, 744–753 (2021)

    Article  Google Scholar 

  24. D. Watts, S. Strogatz, Collective dynamics of small-world networks. Nature 393, 440–442 (1998)

    Article  ADS  MATH  Google Scholar 

  25. P. Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Math. Biosci. 180, 29–48 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  26. M. Raissi, P. Perdikaris, G. Karniadakis, Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  27. D. Kingma, J. Ba, Adam: a method for stochastic optimization. ArXiv Preprint ArXiv:1412.6980. (2014)

Download references

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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