Investigating the Relationship between the Built Environment and Relative Risk of COVID-19 in Hong Kong

Huang, Jianwei; Kwan, Mei‐Po; Kan, Zihan; Wong, Man Sing; Kwok, Coco Yin Tung; Yu, Xinyu

doi:10.3390/ijgi9110624

Cited by 75 publications

(73 citation statements)

References 55 publications

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“…Three out of six studies showed positive associations between accessibility to public transit and virus infection [ 37 , 38 , 42 ]. Public transit conveys many passengers for daily commuting or other activities in confined and often crowded settings.…”

Section: Discussionmentioning

confidence: 99%

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

Wang

et al. 2021

IJERPH

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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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Section: Discussionmentioning

confidence: 99%

“…Spatial error model Road density is significant explanatory variables. 8 Huang, Kwan [ 37 ] China Cross-sectional TPU 291 Incidence 1. Population density 2.…”

Section: Table A1mentioning

confidence: 99%

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

Wang

et al. 2021

IJERPH

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“…However, knowledge about the spread of SARS-CoV-2 in controlled environments on the human transmission of COVID-19 is still insufficient, and other factors may also determine the incidence of COVID-19. Huang et al (2020) investigated the associations between several built-environment variables (e.g. accessibility and green space density) and COVID-19 risk using global Poisson regression and geographically weighted Poisson regression models.…”

Section: Discussionmentioning

confidence: 99%

On the usefulness of the bioclimatic correlative models of SARS-CoV-2

Gutiérrez-Hernández

García

2021

Environmental Research

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This paper addresses the effects of atmospheric conditions on the spread of the SARS-CoV-2 coronavirus and its associated disease, COVID-19. We assess the limitations of bioclimatic correlative models to explain the geographic distribution of SARS-CoV-2 in the context of medical geography. There is a broad consensus that the global distribution of COVID-19 is not random but conditioned by environmental drivers. However, as the COVID-19 distribution becomes global, including tropical climates, the evidence reveals that atmospheric conditions explain, at most, only a limited amount of the space-time dynamics of SARS-CoV-2. The usefulness of approaches based on bioclimatic envelopes is in question since the dominant route for the spread of COVID-19 seems to be the anthroposphere's non-stationary environment. There is a need to clarify further the role of different transmission routes at multiple scales and outdoor and indoor environments beyond bioclimatic envelopes. At this time, the possible influence of the weather in COVID-19 spread is not sufficient to be taken into account in public health policies. Until reliable bioclimatic envelopes of SARS-CoV-2, if any, are found, caution should be exercised when reporting, as this could have unforeseen consequences, mainly when conclusions are based on interim results that have not been subjected to peer review.

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“…Much research has demonstrated that the occurrence and space‐time development of diseases are significantly influenced by people's socioeconomic characteristics and local built‐environment features (Huang et al., 2020 ; Kwok et al., 2021 ; Tiwari & Rushton, 2010 ). In the context of COVID‐19, it was found by Mollalo, Vahedi, and Rivera ( 2020 ) that median household income, income inequality, percentage of Black female population, and percentage of nurse practitioners were associated with significant variations in COVID‐19 incidence.…”

Section: Introductionmentioning

confidence: 99%

Comparing the space‐time patterns of high‐risk areas in different waves of COVID‐19 in Hong Kong

Kan

Kwan

Huang

et al. 2021

Transactions in GIS

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This study compares the space‐time patterns and characteristics of high‐risk areas of COVID‐19 transmission in Hong Kong between January 23 and April 14 (the first and second waves) and between July 6 and August 29 (the third wave). Using space‐time scan statistics and the contact tracing data of individual confirmed cases, we detect the clusters of residences of, and places visited by, both imported and local cases. We also identify the built‐environment and demographic characteristics of the high‐risk areas during different waves of COVID‐19. We find considerable differences in the space‐time patterns and characteristics of high‐risk residential areas between waves. However, venues and buildings visited by the confirmed cases in different waves have similar characteristics. The results can inform policymakers to target mitigation measures in high‐risk areas and at vulnerable groups, and provide guidance to the public to avoid visiting and conducting activities at high‐risk places.

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Investigating the Relationship between the Built Environment and Relative Risk of COVID-19 in Hong Kong

Cited by 75 publications

References 55 publications

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

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

On the usefulness of the bioclimatic correlative models of SARS-CoV-2

Comparing the space‐time patterns of high‐risk areas in different waves of COVID‐19 in Hong Kong

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