Density or connectivity: What are the main causes of the spatial proliferation of covid-19 in Korea?

Yun Jo, Andy Hong, Hyungun Sung

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


COVID-19 has sparked a debate on the vulnerability of densely populated cities. Some studies argue that high-density urban centers are more vulnerable to infectious diseases due to a higher chance of infection in crowded urban environments. Other studies, however, argue that connectivity rather than population density plays a more significant role in the spread of COVID-19. While several studies have examined the role of urban density and connectivity in Europe and the U.S., few studies have been conducted in Asian countries. This study aims to investigate the role of urban spatial structure on COVID-19 by comparing different measures of urban density and connectivity during the first eight months of the outbreak in Korea. Two measures of density were derived from the Korean census, and four measures of connectivity were computed using social network analysis of the Origin-Destination data from the 2020 Korea Transport Database. We fitted both OLS and negative binomial models to the number of confirmed COVID-19 patients and its infection rates at the county level, collected individually from regional government websites in Korea. Results show that both density and connectivity play an important role in the proliferation of the COVID-19 outbreak in Korea. However, we found that the connectivity measure, particularly a measure of network centrality, was a better indicator of COVID-19 proliferation than the density measures. Our findings imply that policies that take into account different types of connectivity between cities might be necessary to contain the outbreak in the early phase.

Original languageEnglish
Article number5084
JournalInternational Journal of Environmental Research and Public Health
Issue number10
StatePublished - 2021 May 2


  • COVID-19
  • Connectivity
  • Density
  • Negative binomial regression
  • Social network analysis
  • Spatial proliferation


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