IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i18p6712-d413791.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/18/6712/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/18/6712/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Zijing Ye & Ruisi Li & Jing Wu, 2022. "Dynamic Demand Evaluation of COVID-19 Medical Facilities in Wuhan Based on Public Sentiment," IJERPH, MDPI, vol. 19(12), pages 1-22, June.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yeran Sun & Hongchao Fan & Ming Li & Alexander Zipf, 2016. "Identifying the city center using human travel flows generated from location-based social networking data," Environment and Planning B, , vol. 43(3), pages 480-498, May.
    2. Wenting Yang & Jiantong Zhang & Ruolin Ma, 2020. "The Prediction of Infectious Diseases: A Bibliometric Analysis," IJERPH, MDPI, vol. 17(17), pages 1-19, August.
    3. Rezapour, Shabnam & Baghaian, Atefe & Naderi, Nazanin & Sarmiento, Juan P., 2023. "Infection transmission and prevention in metropolises with heterogeneous and dynamic populations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 113-138.
    4. Sparks, Kevin & Moehl, Jessica & Weber, Eric & Brelsford, Christa & Rose, Amy, 2022. "Shifting temporal dynamics of human mobility in the United States," Journal of Transport Geography, Elsevier, vol. 99(C).
    5. Miaoyi Li & Lei Dong & Zhenjiang Shen & Wei Lang & Xinyue Ye, 2017. "Examining the Interaction of Taxi and Subway Ridership for Sustainable Urbanization," Sustainability, MDPI, vol. 9(2), pages 1-12, February.
    6. Jiang, Jincheng & Xu, Zhihua & Zhang, Zhenxin & Zhang, Jie & Liu, Kang & Kong, Hui, 2023. "Revealing the fractal and self-similarity of realistic collective human mobility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    7. Tardy, Olivia & Lenglos, Christophe & Lai, Sandra & Berteaux, Dominique & Leighton, Patrick A., 2023. "Rabies transmission in the Arctic: An agent-based model reveals the effects of broad-scale movement strategies on contact risk between Arctic foxes," Ecological Modelling, Elsevier, vol. 476(C).
    8. Jung Hyun Woo, 2021. "Classification of TOD Typologies Based on Pedestrian Behavior for Sustainable and Active Urban Growth in Seoul," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    9. Li, Jingjing & Kim, Changjoo & Sang, Sunhee, 2018. "Exploring impacts of land use characteristics in residential neighborhood and activity space on non-work travel behaviors," Journal of Transport Geography, Elsevier, vol. 70(C), pages 141-147.
    10. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    11. Floriana Gargiulo & Sônia Ternes & Sylvie Huet & Guillaume Deffuant, 2010. "An Iterative Approach for Generating Statistically Realistic Populations of Households," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-9, January.
    12. Saturnino Luz & Masood Masoodian, 2022. "Exploring Environmental and Geographical Factors Influencing the Spread of Infectious Diseases with Interactive Maps," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    13. Liu, Yan & Wang, Siqin & Xie, Bin, 2019. "Evaluating the effects of public transport fare policy change together with built and non-built environment features on ridership: The case in South East Queensland, Australia," Transport Policy, Elsevier, vol. 76(C), pages 78-89.
    14. Clarke, Philippa & Ailshire, Jennifer A. & Lantz, Paula, 2009. "Urban built environments and trajectories of mobility disability: Findings from a national sample of community-dwelling American adults (1986-2001)," Social Science & Medicine, Elsevier, vol. 69(6), pages 964-970, September.
    15. Jeong-Hui Park & Eunhye Yoo & Youngdeok Kim & Jung-Min Lee, 2021. "What Happened Pre- and during COVID-19 in South Korea? Comparing Physical Activity, Sleep Time, and Body Weight Status," IJERPH, MDPI, vol. 18(11), pages 1-13, May.
    16. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    17. Matteo Böhm & Mirco Nanni & Luca Pappalardo, 2022. "Gross polluters and vehicle emissions reduction," Nature Sustainability, Nature, vol. 5(8), pages 699-707, August.
    18. Su, Rongxiang & Xiao, Jingyi & McBride, Elizabeth C. & Goulias, Konstadinos G., 2021. "Understanding senior's daily mobility patterns in California using human mobility motifs," Journal of Transport Geography, Elsevier, vol. 94(C).
    19. Pablo D. Fajgelbaum & Amit Khandelwal & Wookun Kim & Cristiano Mantovani & Edouard Schaal, 2021. "Optimal Lockdown in a Commuting Network," American Economic Review: Insights, American Economic Association, vol. 3(4), pages 503-522, December.
    20. Robert Stewart & Marie Urban & Samantha Duchscherer & Jason Kaufman & April Morton & Gautam Thakur & Jesse Piburn & Jessica Moehl, 2016. "A Bayesian machine learning model for estimating building occupancy from open source data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1929-1956, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:18:p:6712-:d:413791. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.