COVID-19 Risk Assessment for the Tokyo Olympic Games

  1. Wenhui Zhu
  2. Jie Feng
  3. Cheng Li
  4. Huimin Wang
  5. Yang Zhong
  6. Lijun Zhou
  7. Xingyu Zhang
  8. Tao Zhang

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Abstract

Introduction: As of June 7, 2021, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread to more than 200 countries. The global number of reported cases is more than 172.9 million, with more than 3.7 million deaths, and the number of infected individuals is still growing rapidly. Consequently, events and activities around the world were canceled or postponed, and the preparation for sporting events were greatly challenged. Under such circumstances, about 11,000 athletes from ~206 countries are arriving in Tokyo for the 32nd Summer Olympic Games. Therefore, it is urgently necessary to assess the occurrence and spread risk of COVID-19 for the Games.

Objectives: To explore effective prevention and control measures for COVID-19 in large international events through simulations of different interventions according to risk assessment.

Methods: We used a random model to calculate the number of initial infected patients and used Poisson distribution to determine the number of initial infected patients based on the number of countries involved. Furthermore, to simulate the COVID-19 transmission, the susceptible-exposed-symptomatic-asymptomatic-recovered-hospitalized (SEIARH) model was established based on the susceptible-exposed-infectious-recovered (SEIR) mathematical model of epidemic diseases. According to risk assessment indicators produced by different scenarios of the simulated interventions, the risk of COVID-19 transmission in Tokyo Olympic Games was assessed.

Results: The current COVID-19 prevention measures proposed by the Japan Olympic Committee need to be enhanced. And large-scale vaccination will effectively control the spread of COVID-19. When the protective efficacy of vaccines is 78.1% or 89.8%, and if the vaccination rate of athletes reaches 80%, an epidemic prevention barrier can be established.

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  1. Version published to 10.3389/fpubh.2021.730611
  2. ScreenIT

    SciScore for 10.1101/2021.04.14.21255261: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All the above statistical analyses were performed in Matlab R 2016a.
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The limitation of this study was that there was no specific number of participants from each country, so it was not possible to accurately estimate the initially infected individuals, but we have given the calculation formula. Secondly, this paper assumed that the effective rate of vaccine was 100%, which was not in line with the actual situation, but we solve the problem of vaccine efficiency indirectly through different vaccination rates. But after the IOC published the specific data and obtained the effective rate of the vaccine, we can carry out accurate transmission simulation.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2021.04.14.21255261v1 on medRxiv