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

  1. Moritz U. G. Kraemer
  2. Chia-Hung Yang
  3. Bernardo Gutierrez
  4. Chieh-Hsi Wu
  5. Brennan Klein
  6. David M. Pigott
  7. Open COVID-19 Data Working Group
  8. Louis du Plessis
  9. Nuno R. Faria
  10. Ruoran Li
  11. William P. Hanage
  12. John S. Brownstein
  13. Maylis Layan
  14. Alessandro Vespignani
  15. Huaiyu Tian
  16. Christopher Dye
  17. Oliver G. Pybus
  18. Samuel V. Scarpino

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Abstract

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 et al. 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.

Science , this issue p. 493

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  2. Version published to 10.1126/science.abb4218
  3. Version published to 10.1101/2020.03.02.20026708 on medRxiv