“Distance‐Driven” Versus “Density‐Driven”: Understanding the Role of “Source‐Case” Distance and Gathering Places in the Localized Spatial Clustering of COVID‐19—A Case Study of the Xinfadi Market, Beijing (China)

Sui Zhang, Zhao Yang, Minghao Wang, Baolei Zhang

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Abstract

The frequent occurrence of local COVID-19 today gives a strong necessity to better understand the effects of "source-case" distance and gathering places, which are often considered to be the key factors of the localized spatial clustering of an epidemic. In this study, the localized spatial clustering of COVID-19 cases, which originated in the Xinfadi market in Beijing from June–July 2020, was investigated by exploring the spatiotemporal characteristics of the clustering using descriptive statistics, point pattern analysis, and spatial autocorrelation calculation approaches. Spatial lag zero-inflated negative binomial regression model and geographically weighted Poisson regression with spatial effects were also introduced to explore the factors which influenced the clustering of COVID-19 cases at the micro spatial scale. It was found that the local epidemic can be significantly divided into two stages which are asymmetric in time. A significant spatial spillover effect of COVID-19 was identified in both global and local modeling estimation. The dominant role of the “source-case” distance effect, which was reflected in both global and local scales, was revealed. Relatively, the role of gathering places is not significant at the initial stage of the epidemic, but the upward trend of the significance of some places is obvious. The trend from "distance-driven" to "density-driven" of the localized spatial clustering of COVID-19 was predicted. The effectiveness of blocking the transformation trend will be a key issue for the global response to the local COVID-19.
Original languageEnglish
Article numbere2021GH000458
JournalGeoHealth
Volume5
Issue number8
DOIs
Publication statusPublished - 1 Aug 2021
Externally publishedYes

Keywords

  • COVID-19
  • Gathering place
  • Distance
  • Zero-inflated model
  • Geographically weighted Poisson regression
  • Xinfadi market
  • Beijing

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