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Response to COVID-19 during the Tokyo Olympic Games: Did we properly assess the risk?
- Source :
- Epidemics, Vol 40, Iss , Pp 100618- (2022)
- Publication Year :
- 2022
- Publisher :
- Elsevier, 2022.
-
Abstract
- Background: The number of coronavirus disease 2019 (COVID-19) cases was expected to increase during the Tokyo Olympic Games because of the increased physical contact within and between the domestic population and international participants of the Games. The rapid rise of the Delta variant (B.1.617) in Japan meant that hosting the Olympic Games without any restrictions was likely to lead to an increase in cases. We aimed to quantitatively assess possible COVID-19 response strategies for the Olympic Games, comparing the prevalence of severe cases and the cumulative number of COVID-19 deaths via scenario analysis. Methods: We used a discrete-time deterministic compartmental model structured by age group. Parameters were calibrated using the age-stratified COVID-19 incidence data in Osaka. Numerical simulations incorporated the planned Olympics Games and nationwide COVID-19 vaccination into the proposed model, alongside various subjects and types of countermeasures. Results: Our model-informed approach suggested that having spectators at the Tokyo Olympic Games could lead to a surge in both cases and hospitalization. Projections for the scenario that explicitly incorporated the spread of the Delta variant (i.e., time-dependent increase in the relative transmissibility) showed that imposing stringent social distancing measures (Rt=0.7) for more than 8 weeks from the end of the Olympic Games might be required to suppress the prevalence of severe cases of COVID-19 to avoid overwhelming the intensive care unit capacity in Tokyo. Conclusions: Our modeling analyses guided an optimal choice of COVID-19 response during and after the Tokyo Olympic Games, allowing the epidemic to be brought under control despite such a large mass gathering.
Details
- Language :
- English
- ISSN :
- 17554365
- Volume :
- 40
- Issue :
- 100618-
- Database :
- Directory of Open Access Journals
- Journal :
- Epidemics
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.1e0a5052b19b42d3a5234504528cbd3e
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.epidem.2022.100618