Built environment and the metropolitan pandemic: Analysis of the COVID-19 spread in Hong Kong.
Identifieur interne : 000224 ( Main/Exploration ); précédent : 000223; suivant : 000225Built environment and the metropolitan pandemic: Analysis of the COVID-19 spread in Hong Kong.
Auteurs : Tsz Leung Yip [Hong Kong] ; Yaoxuan Huang [Hong Kong] ; Cong Liang [Hong Kong]Source :
- Building and environment [ 0360-1323 ] ; 2020.
Abstract
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.
DOI: 10.1016/j.buildenv.2020.107471
PubMed: 33250560
PubMed Central: PMC7678484
Affiliations:
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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.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">33250560</PMID><DateRevised><Year>2020</Year><Month>12</Month><Day>01</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0360-1323</ISSN><JournalIssue CitedMedium="Print"><PubDate><Year>2020</Year><Month>Nov</Month><Day>20</Day></PubDate></JournalIssue><Title>Building and environment</Title><ISOAbbreviation>Build Environ</ISOAbbreviation></Journal><ArticleTitle>Built environment and the metropolitan pandemic: Analysis of the COVID-19 spread in Hong Kong.</ArticleTitle><Pagination><MedlinePgn>107471</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.buildenv.2020.107471</ELocationID><Abstract><AbstractText>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.</AbstractText><CopyrightInformation>© 2020 Elsevier Ltd. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Yip</LastName><ForeName>Tsz Leung</ForeName><Initials>TL</Initials><AffiliationInfo><Affiliation>Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Huang</LastName><ForeName>Yaoxuan</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liang</LastName><ForeName>Cong</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Lee Shau Kee School of Business and Administration, The Open University of Hong Kong, Hong Kong.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>11</Month><Day>20</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Build Environ</MedlineTA><NlmUniqueID>101562928</NlmUniqueID><ISSNLinking>0360-1323</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Built environment</Keyword><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Census data</Keyword><Keyword MajorTopicYN="N">Social distancing measures</Keyword></KeywordList><CoiStatement>The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Fig. B.1Test of Cox model assumption for N_Clinic (phase 1)Fig. B.1 Fig. B.2Test of Cox model assumption for D_Restaurant (phase 1)Fig. B.2 Fig. B.3Residual - versus - fitted plot for testing the heteroscedasticity (phase 1)Fig. B.3 Fig. B.4Q-Q plot of residuals for OLS (phase 1)Fig. B.4 Fig. B.5Test of Cox model assumption for D_MTRE (phase 2)Fig. B.5 Fig. B.6Test of Cox model assumption for D_Public_markets (phase 2)Fig. B.6 Fig. B.7Test of Cox model assumption for N_Public_markets (phase 2)Fig. B.7 Fig. B.8Test of Cox model assumption for N_Clinic (phase 2)Fig. B.8 Fig. B.9Residual - versus - fitted plot for testing the heteroscedasticity (phase 2)Fig. B.9 Fig. B.10Q-Q plot of residuals for OLS (phase 2)Fig. B.10</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>09</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>11</Month><Day>11</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>11</Month><Day>13</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>11</Month><Day>30</Day><Hour>5</Hour><Minute>51</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>12</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>12</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33250560</ArticleId><ArticleId IdType="doi">10.1016/j.buildenv.2020.107471</ArticleId><ArticleId IdType="pii">S0360-1323(20)30838-6</ArticleId><ArticleId IdType="pmc">PMC7678484</ArticleId></ArticleIdList><pmc-dir>pmcsd</pmc-dir>
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