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Associating COVID-19 Severity with Urban Factors: A Case Study of Wuhan

Author

Listed:
  • Xin Li

    (School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China
    School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Lin Zhou

    (School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Tao Jia

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China)

  • Ran Peng

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

  • Xiongwu Fu

    (Information Department, Wuhan Land Use and Urban Spatial Planning Research Center, Wuhan 430010, China)

  • Yuliang Zou

    (School of Health Sciences, Wuhan University, Wuhan 430071, China)

Abstract

Wuhan encountered a serious attack in the first round of the coronavirus disease 2019 (COVID-19) pandemic, which has resulted in a public health social impact, including public mental health. Based on the Weibo help data, we inferred the spatial distribution pattern of the epidemic situation and its impacts. Seven urban factors, i.e., urban growth, general hospital, commercial facilities, subway station, land-use mixture, aging ratio, and road density, were selected for validation with the ordinary linear model, in which the former six factors presented a globally significant association with epidemic severity. Then, the geographically weighted regression model (GWR) was adopted to identify their unevenly distributed effects in the urban space. Among the six factors, the distribution and density of major hospitals exerted significant effects on epidemic situation. Commercial facilities appear to be the most prevalently distributed significant factor on epidemic situation over the city. Urban growth, in particular the newly developed residential quarters with high-rise buildings around the waterfront area of Hanyang and Wuchang, face greater risk of the distribution. The influence of subway stations concentrates at the adjacency place where the three towns meet and some near-terminal locations. The aging ratio of the community dominantly affects the hinterland of Hankou to a broader extent than other areas in the city. Upon discovering the result, a series of managerial implications that coordinate various urban factors were proposed. This research may contribute toward developing specific planning and design responses for different areas in the city based on a better understanding of the occurrence, transmission, and diffusion of the COVID-19 epidemic in the metropolitan area.

Suggested Citation

  • Xin Li & Lin Zhou & Tao Jia & Ran Peng & Xiongwu Fu & Yuliang Zou, 2020. "Associating COVID-19 Severity with Urban Factors: A Case Study of Wuhan," IJERPH, MDPI, vol. 17(18), pages 1-20, September.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:18:p:6712-:d:413791
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    References listed on IDEAS

    as
    1. Cervero, Robert, 1996. "Mixed land-uses and commuting: Evidence from the American Housing Survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(5), pages 361-377, September.
    2. J. O. Lloyd-Smith & S. J. Schreiber & P. E. Kopp & W. M. Getz, 2005. "Superspreading and the effect of individual variation on disease emergence," Nature, Nature, vol. 438(7066), pages 355-359, November.
    3. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    4. Daniel P. McMillen, 2004. "Geographically Weighted Regression: The Analysis of Spatially Varying Relationships," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 554-556.
    5. Christopher S. Carlson & Michael A. Eberle & Leonid Kruglyak & Deborah A. Nickerson, 2004. "Mapping complex disease loci in whole-genome association studies," Nature, Nature, vol. 429(6990), pages 446-452, May.
    6. Stephen Eubank & Hasan Guclu & V. S. Anil Kumar & Madhav V. Marathe & Aravind Srinivasan & Zoltán Toroczkai & Nan Wang, 2004. "Modelling disease outbreaks in realistic urban social networks," Nature, Nature, vol. 429(6988), pages 180-184, May.
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    Cited by:

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    2. Kangwei Tu & Andras Reith, 2023. "Changes in Urban Planning in Response to Pandemics: A Comparative Review from H1N1 to COVID-19 (2009–2022)," Sustainability, MDPI, vol. 15(12), pages 1-20, June.
    3. Alireza Dehghani & Mehdi Alidadi & Ayyoob Sharifi, 2022. "Compact Development Policy and Urban Resilience: A Critical Review," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    4. Marija Jevtic & Vlatka Matkovic & Milica Paut Kusturica & Catherine Bouland, 2022. "Build Healthier: Post-COVID-19 Urban Requirements for Healthy and Sustainable Living," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    5. Jingjing Wang & Xueying Wu & Ruoyu Wang & Dongsheng He & Dongying Li & Linchuan Yang & Yiyang Yang & Yi Lu, 2021. "Review of Associations between Built Environment Characteristics and Severe Acute Respiratory Syndrome Coronavirus 2 Infection Risk," IJERPH, MDPI, vol. 18(14), pages 1-16, July.
    6. Nushrat Nazia & Zahid Ahmad Butt & Melanie Lyn Bedard & Wang-Choi Tang & Hibah Sehar & Jane Law, 2022. "Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review," IJERPH, MDPI, vol. 19(14), pages 1-28, July.
    7. Qiang Niu & Wanxian Wu & Jie Shen & Jiaxin Huang & Qiling Zhou, 2021. "Relationship between Built Environment and COVID-19 Dispersal Based on Age Stratification: A Case Study of Wuhan," IJERPH, MDPI, vol. 18(14), pages 1-17, July.

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