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

Abstract

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

Keywords

Affiliated Institutions

• University of Southampton GB
• Centre National de la Recherche Scientifique FR
• Harvard University US
• Royal Veterinary College GB
• Boston Children's Hospital US
• Universidad San Francisco de Quito EC
• Sorbonne Université FR
• Beijing Normal University CN
• Université Sorbonne Nouvelle FR
• Institute for Health Metrics and Evaluation US
• Northeastern University US
• Institut Pasteur FR
• University of Oxford GB
• Institute for Scientific Interchange IT
• State Key Laboratory of Remote Sensing Science CN

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