The Superspreading Places of COVID-19 and the Associated Built-environment and Socio-demographic Features: A Study Using a Spatial Network Framework and Individual-level Activity Data

Keywords: COVID-19 pandemic, high-risk places, superspreading places, individual-level activity data, spatial network, built environment
Session Type: Virtual Paper Abstract
Day: Friday
Session Start / End Time: 2/25/2022 12:40 PM (Eastern Time (US & Canada)) - 2/25/2022 02:00 PM (Eastern Time (US & Canada))
Room: Virtual 18

Abstract

Previous studies observed that most COVID-19 infections were transmitted by a few individuals at a few high-risk places (e.g., bars or social gathering venues). These individuals, often called superspreaders, transmit the virus to an unexpectedly large number of people. Further, a small number of superspreading places (SSPs) where this occurred account for a large number of COVID-19 transmissions. In this study, we propose a spatial network framework for identifying the SSPs that disproportionately spread COVID-19. Using individual-level activity data of the confirmed cases in Hong Kong, we first identify the high-risk places in the first four COVID-19 waves using the space-time kernel density method (STKDE). Then, we identify the SSPs among these high-risk places by constructing spatial networks that integrate the flow intensity of the confirmed cases. We also examine what built-environment and socio-demographic features would make a high-risk place to more likely become an SSP in different waves of COVID-19 by using regression models. The results indicate that some places had very high transmission risk and suffered from repeated COVID-19 outbreaks over the four waves, and some of these high-risk places were SSPs where most (about 80%) of the COVID-19 transmission occurred due to their intense spatial interactions with other places. Further, we find that high-risk places with dense urban renewal buildings and high median monthly household rent-to-income ratio have higher odds of being SSPs. The results also imply that the associations between built-environment and socio-demographic features with the high-risk places and SSPs are dynamic over time.