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

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The effect of human mobility and control measures on the COVID-19 epidemic in China
Science ( IF 44.7 ) Pub Date : 2020-03-25 , DOI: 10.1126/science.abb4218
Moritz U G Kraemer _{1,

2,

3} , Chia-Hung Yang ₄ , Bernardo Gutierrez _{1,

5} , Chieh-Hsi Wu ₆ , Brennan Klein ₄ , David M Pigott ₇ , , Louis du Plessis ₁ , Nuno R Faria ₁ , Ruoran Li ₈ , William P Hanage ₈ , John S Brownstein _{2,

3} , Maylis Layan _{9,

10} , Alessandro Vespignani _{4,

11} , Huaiyu Tian ₁₂ , Christopher Dye ₁ , Oliver G Pybus _{1,

13} , Samuel V Scarpino ₄

Affiliation

Department of Zoology, University of Oxford, Oxford, UK.
Harvard Medical School, Harvard University, Boston, MA, USA.
Boston Children's Hospital, Boston, MA, USA.
Network Science Institute, Northeastern University, Boston, MA, USA.
School of Biological and Environmental Sciences, Universidad San Francisco de Quito USFQ, Quito, Ecuador.
Mathematical Sciences, University of Southampton, Southampton, UK.
Institute for Health Metrics and Evaluation, Department of Health Metrics, University of Washington, Seattle, WA, USA.
Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
Sorbonne Université, Paris, France.
ISI Foundation, Turin, Italy.
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK.

Tracing infection from mobility data 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 Mobile phone data show that the spread of COVID-19 in China was driven by travel and mitigated substantially by local control measures. The ongoing coronavirus disease 2019 (COVID-19) outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions were undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We used real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation in transmission in cities across China and to ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. After the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside of Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.

更新日期：2020-03-25

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