Abstract
Understanding the relationship between the built environment and the risk of COVID-19 transmission is essential to respond to the pandemic. This study explores the relationship between the built environment and COVID-19 risk using the confirmed cases data collected in Hong Kong. Using the information on the residential buildings and places visited for each case from the dataset, we assess the risk of COVID-19 and explore their geographic patterns at the level of Tertiary Planning Unit (TPU) based on incidence rate (R1) and venue density (R2). We then investigate the associations between several built-environment variables (e.g., nodal accessibility and green space density) and COVID-19 risk using global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models. The results indicate that COVID-19 risk tends to be concentrated in particular areas of Hong Kong. Using the incidence rate as an indicator to assess COVID-19 risk may underestimate the risk of COVID-19 transmission in some suburban areas. The GPR and GWPR models suggest a close and spatially heterogeneous relationship between the selected built-environment variables and the risk of COVID-19 transmission. The study provides useful insights that support policymakers in responding to the COVID-19 pandemic and future epidemics.
Highlights
Three coronaviruses have emerged in the past two decades (i.e., SARS-CoV, MERS-CoV, and SARS-CoV-2) [1]
The results show that COVID-19 risk declines dramatically over space from the Tertiary Planning Unit (TPU) with the highest risk, indicating that the risk of COVID-19 transmission tends to be concentrated in particular areas of Hong Kong
The geographically weighted Poisson regression (GWPR) models perform better than the global Poisson regression (GPR) regression models, and the results indicate that the relationships between the selected built-environment variables and COVID-19 risk (i.e., R1 and R2) vary spatially across the study area
Summary
Three coronaviruses have emerged in the past two decades (i.e., SARS-CoV, MERS-CoV, and SARS-CoV-2) [1]. To mitigate the pandemic and control its spread in human populations, most governments have implemented drastic intervention measures, which include travel restrictions, stay-at-home orders, school closings, and restrictions of public gatherings [3,4,5] These control strategies seek to mitigate the spread of COVID-19 by forcing or encouraging people to practice social distancing and reducing risky social interactions. Travel restrictions, and stay-at-home orders are non-pharmaceutical interventions for controlling the spread of COVID-19 by reducing the close contact among people and changing their behaviors [6]. This study investigates the relationship between built-environment features and COVID-19 transmission risk in Hong Kong. The GWPR models perform better than the GPR regression models, and the results indicate that the relationships between the selected built-environment variables and COVID-19 risk (i.e., R1 and R2) vary spatially across the study area
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