IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v48y2021i7p1955-1971.html
   My bibliography  Save this article

The interplay of spatial spread of COVID-19 and human mobility in the urban system of China during the Chinese New Year

Author

Listed:
  • Xiaoyan Mu
  • Anthony Gar-On Yeh

    (The 25809University of Hong Kong, Hong Kong SAR)

  • Xiaohu Zhang

Abstract

The rapid spread of infectious diseases is devastating to the healthcare systems of all countries. The dynamics of the spatial spread of epidemic have received considerable scientific attention. However, the understanding of the spatial variation of epidemic severity in the urban system is lagging. Using synchronized epidemic data and human mobility data, integrated with other multiple-sourced data, this study examines the interplay between disease spread of coronavirus disease (COVID-19) and inter-city and intra-city mobility among 319 Chinese cities. The results show a disease spreading process consisting of a major transfer (inter-city) diffusion before the Chinese New Year and a subsequent local (intra-city) diffusion after the Chinese New Year in the urban system of China. The variations in disease incidence between cities are mainly driven by inter-city mobility from Wuhan, the epidemic center of COVID-19. Cities that are closer to the epidemic center and with more population in the urban area will face higher risks of disease incidence. Warm and humid weather could help mitigate the spread of COVID-19. The extensive inter-city and intra-city travel interventions in China have reduced approximately 70% and 40% inter-city and intra-city mobility, respectively, and effectively slowed down the spread of the disease by minimizing human to human transmission together with other disease monitoring, control, and preventive measures. These findings could provide valuable insights into understanding the dynamics of disease spread in the urban system and help to respond to another new wave of pandemic in China and other parts of the world.

Suggested Citation

  • Xiaoyan Mu & Anthony Gar-On Yeh & Xiaohu Zhang, 2021. "The interplay of spatial spread of COVID-19 and human mobility in the urban system of China during the Chinese New Year," Environment and Planning B, , vol. 48(7), pages 1955-1971, September.
  • Handle: RePEc:sae:envirb:v:48:y:2021:i:7:p:1955-1971
    DOI: 10.1177/2399808320954211
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2399808320954211
    Download Restriction: no

    File URL: https://libkey.io/10.1177/2399808320954211?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lauren M Gardner & András Bóta & Karthik Gangavarapu & Moritz U G Kraemer & Nathan D Grubaugh, 2018. "Inferring the risk factors behind the geographical spread and transmission of Zika in the Americas," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 12(1), pages 1-25, January.
    2. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D. With contributions by-Name:Adamowicz,Wiktor, 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304.
    3. Ross Sparks & Tim Keighley & David Muscatello, 2010. "Early warning CUSUM plans for surveillance of negative binomial daily disease counts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(11), pages 1911-1929.
    4. Shengjie Lai & Nick W. Ruktanonchai & Liangcai Zhou & Olivia Prosper & Wei Luo & Jessica R. Floyd & Amy Wesolowski & Mauricio Santillana & Chi Zhang & Xiangjun Du & Hongjie Yu & Andrew J. Tatem, 2020. "Effect of non-pharmaceutical interventions to contain COVID-19 in China," Nature, Nature, vol. 585(7825), pages 410-413, September.
    5. Jayson S. Jia & Xin Lu & Yun Yuan & Ge Xu & Jianmin Jia & Nicholas A. Christakis, 2020. "Population flow drives spatio-temporal distribution of COVID-19 in China," Nature, Nature, vol. 582(7812), pages 389-394, June.
    6. Paolo Bajardi & Chiara Poletto & Jose J Ramasco & Michele Tizzoni & Vittoria Colizza & Alessandro Vespignani, 2011. "Human Mobility Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-8, January.
    7. Vittoria Colizza & Alain Barrat & Marc Barthelemy & Alain-Jacques Valleron & Alessandro Vespignani, 2007. "Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions," PLOS Medicine, Public Library of Science, vol. 4(1), pages 1-16, January.
    8. 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. Mengyue Yuan & Tong Liu & Chao Yang, 2022. "Exploring the Relationship among Human Activities, COVID-19 Morbidity, and At-Risk Areas Using Location-Based Social Media Data: Knowledge about the Early Pandemic Stage in Wuhan," IJERPH, MDPI, vol. 19(11), pages 1-22, May.
    2. Zhangbo Yang & Jiahao Zhang & Shanxing Gao & Hui Wang, 2022. "Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China," IJERPH, MDPI, vol. 19(2), pages 1-17, January.

    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. 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).
    2. 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.
    3. Wang, Peipei & Liu, Haiyan & Zheng, Xinqi & Ma, Ruifang, 2023. "A new method for spatio-temporal transmission prediction of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    4. Panayotis Christidis & Aris Christodoulou, 2020. "The Predictive Capacity of Air Travel Patterns during the Global Spread of the COVID-19 Pandemic: Risk, Uncertainty and Randomness," IJERPH, MDPI, vol. 17(10), pages 1-15, May.
    5. Jürgen Hackl & Thibaut Dubernet, 2019. "Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models," Future Internet, MDPI, vol. 11(4), pages 1-14, April.
    6. Meng, Xin & Guo, Mingxue & Gao, Ziyou & Yang, Zhenzhen & Yuan, Zhilu & Kang, Liujiang, 2022. "The effects of Wuhan highway lockdown measures on the spread of COVID-19 in China," Transport Policy, Elsevier, vol. 117(C), pages 169-180.
    7. Ye, Maoxin & Lyu, Zeyu, 2020. "Trust, risk perception, and COVID-19 infections: Evidence from multilevel analyses of combined original dataset in China," Social Science & Medicine, Elsevier, vol. 265(C).
    8. Fang, Hanming & Wang, Long & Yang, Yang, 2020. "Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China," Journal of Public Economics, Elsevier, vol. 191(C).
    9. Qiushi Chen & Michiko Tsubaki & Yasuhiro Minami & Kazutoshi Fujibayashi & Tetsuro Yumoto & Junzo Kamei & Yuka Yamada & Hidenori Kominato & Hideki Oono & Toshio Naito, 2021. "Using Mobile Phone Data to Estimate the Relationship between Population Flow and Influenza Infection Pathways," IJERPH, MDPI, vol. 18(14), pages 1-32, July.
    10. Sunio, Varsolo & Mateo-Babiano, Iderlina, 2022. "Pandemics as ‘windows of opportunity’: Transitioning towards more sustainable and resilient transport systems," Transport Policy, Elsevier, vol. 116(C), pages 175-187.
    11. Meng, Xin & Guo, Mingxue & Gao, Ziyou & Kang, Liujiang, 2023. "Interaction between travel restriction policies and the spread of COVID-19," Transport Policy, Elsevier, vol. 136(C), pages 209-227.
    12. 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.
    13. Qiang Wang & Min Su & Min Zhang & Rongrong Li, 2021. "Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare," IJERPH, MDPI, vol. 18(11), pages 1-50, June.
    14. Michał Wielechowski & Katarzyna Czech & Łukasz Grzęda, 2020. "Decline in Mobility: Public Transport in Poland in the time of the COVID-19 Pandemic," Economies, MDPI, vol. 8(4), pages 1-24, September.
    15. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    16. Bo Huang & Jionghua Wang & Jixuan Cai & Shiqi Yao & Paul Kay Sheung Chan & Tony Hong-wing Tam & Ying-Yi Hong & Corrine W. Ruktanonchai & Alessandra Carioli & Jessica R. Floyd & Nick W. Ruktanonchai & , 2021. "Integrated vaccination and physical distancing interventions to prevent future COVID-19 waves in Chinese cities," Nature Human Behaviour, Nature, vol. 5(6), pages 695-705, June.
    17. Fang, Guanfu & Tang, Tianyu & Zhao, Fang & Zhu, Ying, 2023. "The social scar of the pandemic: Impacts of COVID-19 exposure on interpersonal trust," Journal of Asian Economics, Elsevier, vol. 86(C).
    18. Tianyi Li & Jiawen Luo & Cunrui Huang, 2021. "Urban Epidemic Hazard Index for Chinese Cities: Why Did Small Cities Become Epidemic Hotspots?," Papers 2103.05189, arXiv.org.
    19. Zhangbo Yang & Jiahao Zhang & Shanxing Gao & Hui Wang, 2022. "Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China," IJERPH, MDPI, vol. 19(2), pages 1-17, January.
    20. Burris, Courtney & Nikolaev, Alexander & Zhong, Shiran & Bian, Ling, 2021. "Network effects in influenza spread: The impact of mobility and socio-economic factors," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).

    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:sae:envirb:v:48:y:2021:i:7:p:1955-1971. 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: SAGE Publications (email available below). General contact details of provider: .

    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.