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Social Contact Patterns in Japan in the COVID-19 Pandemic During and After the Tokyo Olympic Games

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Journal J Glob Health
Date 2022 Dec 3
PMID 36462208
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Abstract

Background: Social contact data in Japan have not been updated since 2011. The main objectives of this study are to report on newly collected social contact data, to study mixing patterns in the context of the COVID-19 pandemic, and to compare the contact patterns during and after mass events like the 2020 Olympic Games, which were held in 2021.

Methods: We compared the number of contacts per day during and after the Olympic Games and on weekdays and weekends; we also compared them with a pre-COVID-19 pandemic social contact study in Japan. Contact matrices consisting of the age-specific average number of contacted persons recorded per day were obtained from the survey data. Reciprocity at the population level was achieved by using a weighted average.

Results: The median number of contacts per day was 3 (interquartile range (IQR) = 1-6). The occurrence of the Olympic Games and the temporal source of data (weekday or weekend) did not change the results substantially. All three matrices derived from this survey showed age-specific assortative mixing patterns like the previous social contact survey.

Conclusions: The frequency of social contact in Japan did not change substantially during the Tokyo Olympic Games. However, the baseline frequency of social mixing declined vs those collected in 2011.

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References
1.
Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y . Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020; 382(13):1199-1207. PMC: 7121484. DOI: 10.1056/NEJMoa2001316. View

2.
Rainey J, Koch D, Chen Y, Yuan J, Cheriyadat A . Using video-analysis technology to estimate social mixing and simulate influenza transmission at a mass gathering. Epidemics. 2021; 36:100466. DOI: 10.1016/j.epidem.2021.100466. View

3.
Backer J, Mollema L, Vos E, Klinkenberg D, van der Klis F, de Melker H . Impact of physical distancing measures against COVID-19 on contacts and mixing patterns: repeated cross-sectional surveys, the Netherlands, 2016-17, April 2020 and June 2020. Euro Surveill. 2021; 26(8). PMC: 7908067. DOI: 10.2807/1560-7917.ES.2021.26.8.2000994. View

4.
Hens N, Ayele G, Goeyvaerts N, Aerts M, Mossong J, Edmunds J . Estimating the impact of school closure on social mixing behaviour and the transmission of close contact infections in eight European countries. BMC Infect Dis. 2009; 9:187. PMC: 2799408. DOI: 10.1186/1471-2334-9-187. View

5.
Dickens B, Koo J, Lim J, Park M, Quaye S, Sun H . Modelling lockdown and exit strategies for COVID-19 in Singapore. Lancet Reg Health West Pac. 2021; 1:100004. PMC: 7395828. DOI: 10.1016/j.lanwpc.2020.100004. View