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

Bibliographic Data

ID23344259
AuthorsMoritz U G Kraemer (0000-0001-8838-7147, University of Oxford, corresponding author), Chia-Hung Yang (0000-0002-4936-808X, Network Science Institute, Northeastern University, Boston, MA, USA.), B Gutierrez (0000-0002-9220-2739, University of Oxford), Chiu-Hui Wu (0000-0001-9386-725X, Mathematical Sciences, University of Southampton, Southampton, UK.), Brennan Klein (0000-0001-8326-5044, Network Science Institute, Northeastern University, Boston, MA, USA.), David M Pigott (0000-0002-6731-4034, Institute for Health Metrics and Evaluation, Department of Health Metrics, University of Washington, Seattle, WA, USA.), Louis du Plessis (0000-0003-0352-6289, University of Oxford), Nuno Rodrigues Faria (0000-0001-8839-2798, University of Oxford), Ruoran Li (0000-0001-7575-2758, Harvard T.H. Chan School of Public Health, Boston, MA, USA.), William P Hanage (0000-0002-6319-7336, Harvard T.H. Chan School of Public Health, Boston, MA, USA.), John S Brownstein (0000-0001-8568-5317, Harvard Medical School, Harvard University, Boston, MA, USA.), Maylis Layan (0000-0003-3092-686X, Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.), Alessandro Vespignani (0000-0003-3419-4205, Network Science Institute, Northeastern University, Boston, MA, USA.), Huaiyu Tian (0000-0002-4466-0858, State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.), Cristina Dye (0000-0002-2957-1793, University of Oxford), Oliver G Pybus (0000-0002-8797-2667, University of Oxford, corresponding author), Samuel V Scarpino (0000-0001-5716-2770, Network Science Institute, Northeastern University, Boston, MA, USA., corresponding author)
Year2020
Volume368
Issue6490
Pages493-497
Publication date2020-05-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueScience (JOURNAL)
Journal identifiersISSN: 0036-8075 • E-ISSN: 0193-4511
PublisherAmerican Association for the Advancement of Science (AAAS) (PUBLISHER)
DOI10.1126/science.abb4218
PMID32213647
OpenAlexW3013594674
LanguageEN
Citations received417
References cited35

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

China · Coronavirus disease 2019 (COVID-19) · Demographics · Disease · Economics · Environmental health · Geography · Infectious disease (medical specialty) · Outbreak · Psychological intervention · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) · Socioeconomics · Transmission (telecommunications) · Computer Science · COVID-19 and Mental Health · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Demography · Medicine · Virology