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The Unequal Effects of Social Distancing Policy on Subway Ridership during the COVID-19 Pandemic in Seoul, South Korea.

Jaeyoung HaSuyoung JoHee-Kyoung NamSung-Ii Cho
Published in: Journal of urban health : bulletin of the New York Academy of Medicine (2022)
In the Republic of Korea, social distancing policies relied on voluntary participation by citizens and exhibited short-term changes. In this situation, the effects of such policies varied depending on each community's capacity to comply. Here, we collected subway ridership data for 294 stations on nine Seoul Metro lines and aggregated the data for each station to the 184 smallest administrative areas. We found that the mean percent change in subway ridership was fitted by an additive model of the log-transformed percent ratio of the restaurant industry (estimated degrees of freedom (EDF) = 3.24, P < 0.001), the Deprivation Index (DI) (EDF = 3.66, P = 0.015), and the proportion of essential workers (β =  - 0.10 (95% confidence interval - 0.15 to - 0.05, P < 0.001). We found a distinct decrease in subway ridership only in the least deprived areas, suggesting that social distancing is costly.
Keyphrases
  • mental health
  • healthcare
  • public health
  • electronic health record
  • big data
  • physical activity
  • machine learning
  • data analysis
  • artificial intelligence
  • deep learning