Investigating the Role of Urban Factors in COVID-19 Transmission During the Pre- and Post-Omicron Periods: A Case Study of South Korea

Seongyoun Shin and Jaewoong Won ()
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Seongyoun Shin: Department of Smart City Planning and Real Estate, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
Jaewoong Won: Department of Smart City Planning and Real Estate, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea

Sustainability, 2025, vol. 17, issue 5, 1-17

Abstract: While the literature has investigated the associations between urban environments and COVID-19 infection, most studies primarily focused on urban density factors and early outbreaks, often reporting mixed results. We examined how diverse urban factors impact COVID-19 cases across 229 administrative districts in South Korea during Pre-Omicron and Post-Omicron periods. Real-time big data (Wi-Fi, GPS, and credit card transactions) were integrated to capture dynamic mobility and economic activities. Using negative binomial regression and random forest modeling, we analyzed urban factors within the D-variable framework: density (e.g., housing density), diversity (e.g., land-use mix), design (e.g., street connectivity), and destination accessibility (e.g., cultural and community facilities). The results revealed the consistent significance of density and destination-related factors across analytic approaches and transmission phases, but specific factors of significance varied over time. Residential and population densities were more related in the early phase, while employment levels and cultural and community facilities became more relevant in the later phase. Traffic volume and local consumption appeared important, though their significance is not consistent across the models. Our findings highlight the need for adaptive urban planning strategies and public health policies that consider both static and dynamic urban factors to minimize disease risks while sustaining urban vitality and health in the evolving pandemic.

Keywords: COVID-19; omicron; urban environment; D-variable framework; negative binomial regression; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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