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Built environment and the metropolitan pandemic: Analysis of the COVID-19 spread in Hong Kong

The COVID-19 reported initially in December 2019 led to thousands and millions of people infections, deaths at a rapid scale, and a global scale. Metropolitans suffered serious pandemic problems as the built environments of metropolitans contain a large number of people in a relatively small area an...

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Detalles Bibliográficos
Autores principales: Yip, Tsz Leung, Huang, Yaoxuan, Liang, Cong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7678484/
https://www.ncbi.nlm.nih.gov/pubmed/33250560
http://dx.doi.org/10.1016/j.buildenv.2020.107471
Descripción
Sumario:The COVID-19 reported initially in December 2019 led to thousands and millions of people infections, deaths at a rapid scale, and a global scale. Metropolitans suffered serious pandemic problems as the built environments of metropolitans contain a large number of people in a relatively small area and allow frequent contacts to let virus spread through people's contacting with each other. The spread inside a metropolitan is heterogeneous, and we propose that the spatial variation of built environments has a measurable association with the spread of COVID-19. This paper is the pioneering work to investigate the missing link between the built environment and the spread of the COVID-19. In particular, we intend to examine two research questions: (1) What are the association of the built environment with the risk of being infected by the COVID-19? (2) What are the association of the built environment with the duration of suffering from COVID-19? Using the Hong Kong census data, confirmed cases of COVID-19 between January to August 2020 and large size of built environment sample data from the Hong Kong government, our analysis are carried out. The data is divided into two phases before (Phase 1) and during the social distancing measure was relaxed (Phase 2). Through survival analysis, ordinary least squares analysis, and count data analysis, we find that (1) In Phase 1, clinics and restaurants are more likely to influence the prevalence of COVID-19. In Phase 2, public transportation (i.e. MTR), public market, and the clinics influence the prevalence of COVID-19. (2) In Phase 1, the areas of tertiary planning units (i.e., TPU) with more restaurants are found to be positively associated with the period of the prevalence of COVID-19. In Phase 2, restaurants and public markets induce long time occurrence of the COVID-19. (3) In Phase 1, restaurant and public markets are the two built environments that influence the number of COVID-19 confirmed cases. In Phase 2, the number of restaurants is positively related to the number of COVID-19 reported cases. It is suggested that governments should not be too optimistic to relax the necessary measures. In other words, the social distancing measure should remain in force until the signals of the COVID-19 dies out.