Assessment of COVID-19 risk and prevention effectiveness among spectators of mass gathering events.

COVID-19 Infection risk Mass gatherings Novel corona virus Quantitative microbial risk assessment

Journal

Microbial risk analysis
ISSN: 2352-3530
Titre abrégé: Microb Risk Anal
Pays: Netherlands
ID NLM: 101728821

Informations de publication

Date de publication:
Aug 2022
Historique:
received: 14 09 2021
revised: 20 02 2022
accepted: 29 03 2022
pubmed: 7 4 2022
medline: 7 4 2022
entrez: 6 4 2022
Statut: ppublish

Résumé

There is a need to evaluate and minimize the risk of novel coronavirus infections at mass gathering events, such as sports. In particular, to consider how to hold mass gathering events, it is important to clarify how the local infection prevalence, the number of spectators, the capacity proportion, and the implementation of preventions affect the infection risk. In this study, we used an environmental exposure model to analyze the relationship between infection risk and infection prevalence, the number of spectators, and the capacity proportion at mass gathering events in football and baseball games. In addition to assessing risk reduction through the implementation of various preventive measures, we assessed how face-mask-wearing proportion affects infection risk. Furthermore, the model was applied to estimate the number of infectors who entered the stadium and the number of newly infected individuals, and to compare them with actual reported cases. The model analysis revealed an 86-95% reduction in the infection risk due to the implementation of face-mask wearing and hand washing. Under conditions in which vaccine effectiveness was 20% and 80%, the risk reduction rates of infection among vaccinated spectators were 36% and 96%, respectively. Among the individual measures, face-mask wearing was particularly effective, and the infection risk increased as the face-mask-wearing proportion decreased. A linear relationship was observed between infection risk at mass gathering events and the infection prevalence. Furthermore, the number of newly infected individuals was also dependent on the number of spectators and the capacity proportion independent of the infection prevalence, confirming the importance of considering spectator capacity in infection risk management. These results highlight that it is beneficial for organisers to ensure prevention compliance and to mitigate or limit the number of spectators according to the prevalence of local infection. Both the estimated and reported numbers of newly infected individuals after the events were small, below 10 per 3-4 million spectators, despite a small gap between these numbers.

Identifiants

pubmed: 35382415
doi: 10.1016/j.mran.2022.100215
pii: S2352-3522(22)00015-9
pmc: PMC8969296
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100215

Informations de copyright

© 2022 The Author(s).

Déclaration de conflit d'intérêts

This research project comprises members from two private companies, Kao Corporation and NVIDIA Corporation, Japan. Y.I. and W.N. received financial support from the Kao Corporation until March 2020 in context outside the submitted work. T.Y., M.O., and W.N. have received financial support from the Kao Corporation for a collaborative research project in the context of measures at mass gathering events. T.Y., M.O., and W.N. have received financial support from Yomiuri Giants, the Japan Professional Football League, and the Japan Professional Basketball League. M.M., T.Y., M.O., W.N, and S.I. attended the new coronavirus countermeasures liaison council jointly established by the Nippon Professional Baseball Organization and Japan Professional Football League as experts without any rewards. T.Y., M.O., and W.N. are advisors to the Japan National Stadium. Other authors declare no competing interests. The findings and conclusions of this article are solely the responsibility of the authors and do not represent the official views of any institution.

Auteurs

Tetsuo Yasutaka (T)

Institute for Geo-Resources and Environment, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1, Higashi, Tsukuba, Ibaraki 305-8567, Japan.

Michio Murakami (M)

Department of Health Risk Communication, Fukushima Medical University School of Medicine, 1 Hikarigaoka, Fukushima, Fukushima 960-1295, Japan.
Division of Scientific Information and Public Policy, Center for Infectious Disease Education and Research (CiDER), Osaka University, Techno Alliance C209, 2-8 Yamadaoka, Suita, Osaka 565-0871, Japan.

Yuichi Iwasaki (Y)

Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology (AIST), 16-1, Onogawa, Tsukuba, Ibaraki 305-8569, Japan.

Wataru Naito (W)

Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology (AIST), 16-1, Onogawa, Tsukuba, Ibaraki 305-8569, Japan.

Masaki Onishi (M)

Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan.

Tsukasa Fujita (T)

Institute for Geo-Resources and Environment, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1, Higashi, Tsukuba, Ibaraki 305-8567, Japan.

Seiya Imoto (S)

Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.

Classifications MeSH