Tokyo's COVID-19: An Urban Perspective on Factors Influencing Infection Rates in a Global City

Overview

Journal Sustain Cities Soc

Publisher Elsevier

Date 2023 Jul 3

PMID 37397232

Authors

Mehdi Alidadi

Ayyoob Sharifi

Daisuke Murakami

Affiliations

Soon will be listed here.

Abstract

This research investigates the relationship between COVID-19 and urban factors in Tokyo. To understand the spread dynamics of COVID-19, the study examined 53 urban variables (including population density, socio-economic status, housing conditions, transportation, and land use) in 53 municipalities of Tokyo prefecture. Using spatial models, the study analysed the patterns and predictors of COVID-19 infection rates. The findings revealed that COVID-19 cases were concentrated in central Tokyo, with clustering levels decreasing after the outbreaks. COVID-19 infection rates were higher in areas with a greater density of retail stores, restaurants, health facilities, workers in those sectors, public transit use, and telecommuting. However, household crowding was negatively associated. The study also found that telecommuting rate and housing crowding were the strongest predictors of COVID-19 infection rates in Tokyo, according to the regression model with time-fixed effects, which had the best validation and stability. This study's results could be useful for researchers and policymakers, particularly because Japan and Tokyo have unique circumstances, as there was no mandatory lockdown during the pandemic.

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References

Yao Y, Shi W, Zhang A, Liu Z, Luo S . Examining the diffusion of coronavirus disease 2019 cases in a metropolis: a space syntax approach. Int J Health Geogr. 2021; 20(1):17. PMC: 8083925. DOI: 10.1186/s12942-021-00270-4. View

Villalobos Dintrans P, Castillo C, de la Fuente F, Maddaleno M . COVID-19 incidence and mortality in the Metropolitan Region, Chile: Time, space, and structural factors. PLoS One. 2021; 16(5):e0250707. PMC: 8101927. DOI: 10.1371/journal.pone.0250707. View

Li B, Peng Y, He H, Wang M, Feng T . Built environment and early infection of COVID-19 in urban districts: A case study of Huangzhou. Sustain Cities Soc. 2021; 66:102685. PMC: 7836794. DOI: 10.1016/j.scs.2020.102685. View

You H, Wu X, Guo X . Distribution of COVID-19 Morbidity Rate in Association with Social and Economic Factors in Wuhan, China: Implications for Urban Development. Int J Environ Res Public Health. 2020; 17(10). PMC: 7277377. DOI: 10.3390/ijerph17103417. View

Alidadi M, Sharifi A . Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review. Sci Total Environ. 2022; 850:158056. PMC: 9383943. DOI: 10.1016/j.scitotenv.2022.158056. View

De Angelis E, Renzetti S, Volta M, Donato F, Calza S, Placidi D . COVID-19 incidence and mortality in Lombardy, Italy: An ecological study on the role of air pollution, meteorological factors, demographic and socioeconomic variables. Environ Res. 2021; 195:110777. PMC: 7826113. DOI: 10.1016/j.envres.2021.110777. View

Florida R, Mellander C . The geography of COVID-19 in Sweden. Ann Reg Sci. 2021; 68(1):125-150. PMC: 8299438. DOI: 10.1007/s00168-021-01071-0. View

Baser O . Population density index and its use for distribution of Covid-19: A case study using Turkish data. Health Policy. 2020; 125(2):148-154. PMC: 7550260. DOI: 10.1016/j.healthpol.2020.10.003. View

Russette H, Graham J, Holden Z, Semmens E, Williams E, Landguth E . Greenspace exposure and COVID-19 mortality in the United States: January-July 2020. Environ Res. 2021; 198:111195. PMC: 8079939. DOI: 10.1016/j.envres.2021.111195. View

10.

Chan P, Greene S, Lim S, Fine A, Thompson C . Persistent disparities in SARS-CoV-2 test percent positivity by neighborhood in New York City, March 1-July 25, 2020. Ann Epidemiol. 2021; 63:46-51. PMC: 9759881. DOI: 10.1016/j.annepidem.2021.07.032. View

11.

Wang L, Zhang S, Yang Z, Zhao Z, Vernez Moudon A, Feng H . What county-level factors influence COVID-19 incidence in the United States? Findings from the first wave of the pandemic. Cities. 2021; 118:103396. PMC: 8316070. DOI: 10.1016/j.cities.2021.103396. View

12.

Kadi N, Khelfaoui M . Population density, a factor in the spread of COVID-19 in Algeria: statistic study. Bull Natl Res Cent. 2020; 44(1):138. PMC: 7439635. DOI: 10.1186/s42269-020-00393-x. View

13.

Khavarian-Garmsir A, Sharifi A, Moradpour N . Are high-density districts more vulnerable to the COVID-19 pandemic?. Sustain Cities Soc. 2022; 70:102911. PMC: 9760197. DOI: 10.1016/j.scs.2021.102911. View

14.

Alam M, Sultana R . Influences of climatic and non-climatic factors on COVID-19 outbreak: A review of existing literature. Environ Chall (Amst). 2023; 5:100255. PMC: 8383476. DOI: 10.1016/j.envc.2021.100255. View

15.

Song P, Karako T . The strategy behind Japan's response to COVID-19 from 2020-2021 and future challenges posed by the uncertainty of the Omicron variant in 2022. Biosci Trends. 2022; 15(6):350-352. DOI: 10.5582/bst.2021.01560. View

16.

Yang T, Kim S, Zhao Y, Choi S . Examining spatial inequality in COVID-19 positivity rates across New York City ZIP codes. Health Place. 2021; 69:102574. PMC: 8631550. DOI: 10.1016/j.healthplace.2021.102574. View

17.

Tchicaya A, Lorentz N, Leduc K, de Lanchy G . COVID-19 mortality with regard to healthcare services availability, health risks, and socio-spatial factors at department level in France: A spatial cross-sectional analysis. PLoS One. 2021; 16(9):e0256857. PMC: 8448369. DOI: 10.1371/journal.pone.0256857. View

18.

Wu X, Zhang J . Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR). Environ Sci Pollut Res Int. 2021; 28(32):43732-43746. PMC: 8035058. DOI: 10.1007/s11356-021-13653-8. View

19.

Tieskens K, Patil P, Levy J, Brochu P, Lane K, Fabian M . Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts. BMC Infect Dis. 2021; 21(1):686. PMC: 8283097. DOI: 10.1186/s12879-021-06389-w. View

20.

Lak A, Sharifi A, Badr S, Zali A, Maher A, Mostafavi E . Spatio-temporal patterns of the COVID-19 pandemic, and place-based influential factors at the neighborhood scale in Tehran. Sustain Cities Soc. 2022; 72:103034. PMC: 9761301. DOI: 10.1016/j.scs.2021.103034. View