4.3 Review

Review of Associations between Built Environment Characteristics and Severe Acute Respiratory Syndrome Coronavirus 2 Infection Risk

Publisher

MDPI
DOI: 10.3390/ijerph18147561

Keywords

SARS-CoV-2; COVID-19; built environment

Funding

  1. National Natural Science Foundation of China [51778552]
  2. Research Grants Council of the Hong Kong SAR [CityU11207520]

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The COVID-19 pandemic has sparked research interest in its transmission pathways and infection factors, with some studies suggesting a link between built environment attributes and infection risk. However, no systematic review has been conducted on this topic. High-density built environments, easy access to public transportation, and limited green spaces are associated with higher infection risks.
The coronavirus disease 2019 pandemic has stimulated intensive research interest in its transmission pathways and infection factors, e.g., socioeconomic and demographic characteristics, climatology, baseline health conditions or pre-existing diseases, and government policies. Meanwhile, some empirical studies suggested that built environment attributes may be associated with the transmission mechanism and infection risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, no review has been conducted to explore the effect of built environment characteristics on the infection risk. This research gap prevents government officials and urban planners from creating effective urban design guidelines to contain SARS-CoV-2 infections and face future pandemic challenges. This review summarizes evidence from 25 empirical studies and provides an overview of the effect of built environment on SARS-CoV-2 infection risk. Virus infection risk was positively associated with the density of commercial facilities, roads, and schools and with public transit accessibility, whereas it was negatively associated with the availability of green spaces. This review recommends several directions for future studies, namely using longitudinal research design and individual-level data, considering multilevel factors and extending to diversified geographic areas.

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