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. 2023 Oct:97:104743.
doi: 10.1016/j.scs.2023.104743. Epub 2023 Jun 28.

Tokyo's COVID-19: An urban perspective on factors influencing infection rates in a global city

Mehdi Alidadi  1   2 , Ayyoob Sharifi  3 , Daisuke Murakami  4
Affiliations

Tokyo's COVID-19: An urban perspective on factors influencing infection rates in a global city

Mehdi Alidadi et al. Sustain Cities Soc. 2023 Oct.

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.

Keywords: COVID-19; Cities; Spread dynamics; Tokyo; Urban planning; Urban variables.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig 1
Fig. 1
The case study area and the spatial distribution of its population and employment (Authors calculation based on SSDSE data).
Fig 2
Fig. 2
COVID-19 cases and their spatial distribution in Tokyo prefecture during the covered period (Authors calculation based on SSDSE and MHLW data).
Fig 3
Fig. 3
Changes in global Moran's I spatial autocorrelation of COVID-19 infection rate in Tokyo prefecture (Authors calculation based on MHLW data).
Fig 4
Fig. 4
Local Moran's I clusters of cumulative number and infection rate of COVID-19 in Tokyo prefecture (Authors calculation based on MHLW data).
Fig 5
Fig. 5
Absolute value of correlation coefficient of explanatory variables themselves (Authors calculation based on various data sources).
See this image and copyright information in PMC

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