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Modelling Analysis of COVID-19 Transmission and the State of Emergency in Japan

Author

Listed:
  • Zhongxiang Chen

    (College of Engineering and Design, Hunan Normal University, Changsha 410081, China)

  • Zhiquan Shu

    (School of Engineering and Technology, University of Washington, Tacoma, WA 98402, USA)

  • Xiuxiang Huang

    (College of Engineering and Design, Hunan Normal University, Changsha 410081, China)

  • Ke Peng

    (College of Engineering and Design, Hunan Normal University, Changsha 410081, China)

  • Jiaji Pan

    (College of Engineering and Design, Hunan Normal University, Changsha 410081, China
    State Key Laboratory of Developmental Biology of Freshwater Fish, Hunan Normal University, Changsha 410081, China)

Abstract

To assess the effectiveness of the containment strategies proposed in Japan, an SEIAQR (susceptible-exposed-infected-asymptomatic-quarantined-recovered) model was established to simulate the transmission of COVID-19. We divided the spread of COVID-19 in Japan into different stages based on policies. The effective reproduction number R e and the transmission parameters were determined to evaluate the measures conducted by the Japanese Government during these periods. On 7 April 2020, the Japanese authority declared a state of emergency to control the rapid development of the pandemic. Based on the simulation results, the spread of COVID-19 in Japan can be inhibited by containment actions during the state of emergency. The effective reproduction number R e reduced from 1.99 (before the state of emergency) to 0.92 (after the state of emergency). The transmission parameters were fitted and characterized with quantifiable variables including the ratio of untracked cases, the PCR test index and the proportion of COCOA app users (official contact confirming application). The impact of these variables on the control of COVID-19 was investigated in the modelling analysis. On 8 January 2021, the Japanese Government declared another state of emergency. The simulated results demonstrated that the spread could be controlled in May by keeping the same strategies. A higher intensity of PCR testing was suggested, and a larger proportion of COCOA app users should reduce the final number of infections and the time needed to control the spread of COVID-19.

Suggested Citation

  • Zhongxiang Chen & Zhiquan Shu & Xiuxiang Huang & Ke Peng & Jiaji Pan, 2021. "Modelling Analysis of COVID-19 Transmission and the State of Emergency in Japan," IJERPH, MDPI, vol. 18(13), pages 1-15, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:13:p:6858-:d:582732
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    References listed on IDEAS

    as
    1. Yi-Tui Chen & Yung-Feng Yen & Shih-Heng Yu & Emily Chia-Yu Su, 2020. "An Examination on the Transmission of COVID-19 and the Effect of Response Strategies: A Comparative Analysis," IJERPH, MDPI, vol. 17(16), pages 1-14, August.
    2. Zhan, Xiu-Xiu & Liu, Chuang & Zhou, Ge & Zhang, Zi-Ke & Sun, Gui-Quan & Zhu, Jonathan J.H. & Jin, Zhen, 2018. "Coupling dynamics of epidemic spreading and information diffusion on complex networks," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 437-448.
    3. Chad R. Wells & Jeffrey P. Townsend & Abhishek Pandey & Seyed M. Moghadas & Gary Krieger & Burton Singer & Robert H. McDonald & Meagan C. Fitzpatrick & Alison P. Galvani, 2021. "Optimal COVID-19 quarantine and testing strategies," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    4. Djaoue, Seraphin & Guilsou Kolaye, Gabriel & Abboubakar, Hamadjam & Abba Ari, Ado Adamou & Damakoa, Irepran, 2020. "Mathematical modeling, analysis and numerical simulation of the COVID-19 transmission with mitigation of control strategies used in Cameroon," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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    Cited by:

    1. Junsik Park & Gurjoong Kim, 2021. "Risk of COVID-19 Infection in Public Transportation: The Development of a Model," IJERPH, MDPI, vol. 18(23), pages 1-16, December.
    2. Bong Gu Kang & Hee-Mun Park & Mi Jang & Kyung-Min Seo, 2021. "Hybrid Model-Based Simulation Analysis on the Effects of Social Distancing Policy of the COVID-19 Epidemic," IJERPH, MDPI, vol. 18(21), pages 1-17, October.

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