The effect of human mobility and control measures on the COVID-19 epidemic in China

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  • Moritz U. G. Kraemer
    Department of Zoology, University of Oxford, Oxford, UK.
  • Chia-Hung Yang
    Network Science Institute, Northeastern University, Boston, MA, USA.
  • Bernardo Gutierrez
    Department of Zoology, University of Oxford, Oxford, UK.
  • Chieh-Hsi Wu
    Mathematical Sciences, University of Southampton, Southampton, UK.
  • Brennan Klein
    Network Science Institute, Northeastern University, Boston, MA, USA.
  • David M. Pigott
    Institute for Health Metrics and Evaluation, Department of Health Metrics, University of Washington, Seattle, WA, USA.
  • Louis du Plessis
    Department of Zoology, University of Oxford, Oxford, UK.
  • Nuno R. Faria
    Department of Zoology, University of Oxford, Oxford, UK.
  • Ruoran Li
    Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • William P. Hanage
    Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • John S. Brownstein
    Harvard Medical School, Harvard University, Boston, MA, USA.
  • Maylis Layan
    Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
  • Alessandro Vespignani
    Network Science Institute, Northeastern University, Boston, MA, USA.
  • Huaiyu Tian
    State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
  • Christopher Dye
    Department of Zoology, University of Oxford, Oxford, UK.
  • Oliver G. Pybus
    Department of Zoology, University of Oxford, Oxford, UK.
  • Samuel V. Scarpino
    Network Science Institute, Northeastern University, Boston, MA, USA.

Description

<jats:title>Tracing infection from mobility data</jats:title> <jats:p> What sort of measures are required to contain the spread of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19)? The rich data from the Open COVID-19 Data Working Group include the dates when people first reported symptoms, not just a positive test date. Using these data and real-time travel data from the internet services company Baidu, Kraemer <jats:italic>et al.</jats:italic> found that mobility statistics offered a precise record of the spread of SARS-CoV-2 among the cities of China at the start of 2020. The frequency of introductions from Wuhan were predictive of the size of the epidemic sparked in other provinces. However, once the virus had escaped Wuhan, strict local control measures such as social isolation and hygiene, rather than long-distance travel restrictions, played the largest part in controlling SARS-CoV-2 spread. </jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6490" page="493" related-article-type="in-this-issue" vol="368" xlink:href="10.1126/science.abb4218">493</jats:related-article> </jats:p>

Journal

  • Science

    Science 368 (6490), 493-497, 2020-05

    American Association for the Advancement of Science (AAAS)

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