气候变化领域集成服务门户(CCPortal): Predicting the SARS-CoV-2 effective reproduction number using bulk contact data from mobile phones

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DOI	10.1073/pnas.2026731118
	Predicting the SARS-CoV-2 effective reproduction number using bulk contact data from mobile phones
	Rüdiger S.; Konigorski S.; Rakowski A.; Edelman J.A.; Zernick D.; Thieme A.; Lippert C.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:31
英文摘要	Over the last months, cases of SARS-CoV-2 surged repeatedly in many countries but could often be controlled with nonpharmaceutical interventions including social distancing. We analyzed deidentified Global Positioning System (GPS) tracking data from 1.15 to 1.4 million cell phones in Germany per day between March and November 2020 to identify encounters between individuals and statistically evaluate contact behavior. Using graph sampling theory, we estimated the contact index (CX), a metric for number and heterogeneity of contacts. We found that CX, and not the total number of contacts, is an accurate predictor for the effective reproduction number R derived from case numbers. A high correlation between CX and R recorded more than 2 wk later allows assessment of social behavior well before changes in case numbers become detectable. By construction, the CX quantifies the role of superspreading and permits assigning risks to specific contact behavior. We provide a critical CX value beyond which R is expected to rise above 1 and propose to use that value to leverage the social-distancing interventions for the coming months. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	COVID-19; Epidemiology; Network science
语种	英语
scopus关键词	contact examination; epidemiology; Germany; human; mobile phone; physiology; virology; Cell Phone; Contact Tracing; COVID-19; Germany; Humans; SARS-CoV-2
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/238674
作者单位	Machine Leaning Unit, Department of Engineering, NET CHECK GmbH, Berlin, 10829, Germany; Digital Health-Machine Learning, Hasso-Plattner-Institut, Universität Potsdam, Potsdam, 14482, Germany; Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States; Department of Radiation Oncology, Charité-Universitätsmedizin Berlin, Berlin, 13353, Germany; Digital Clinician Scientist Program, Berlin Institute of Health (BIH), Berlin, 10178, Germany; Department of Medicine, Stanford University, Stanford, CA 94305, United States; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, United States
推荐引用方式 GB/T 7714	Rüdiger S.,Konigorski S.,Rakowski A.,et al. Predicting the SARS-CoV-2 effective reproduction number using bulk contact data from mobile phones[J],2021,118(31).
APA	Rüdiger S..,Konigorski S..,Rakowski A..,Edelman J.A..,Zernick D..,...&Lippert C..(2021).Predicting the SARS-CoV-2 effective reproduction number using bulk contact data from mobile phones.Proceedings of the National Academy of Sciences of the United States of America,118(31).
MLA	Rüdiger S.,et al."Predicting the SARS-CoV-2 effective reproduction number using bulk contact data from mobile phones".Proceedings of the National Academy of Sciences of the United States of America 118.31(2021).