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Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)
Science ( IF 44.7 ) Pub Date : 2020-03-16 , DOI: 10.1126/science.abb3221 Ruiyun Li 1 , Sen Pei 2 , Bin Chen 3 , Yimeng Song 4 , Tao Zhang 5 , Wan Yang 6 , Jeffrey Shaman 2
Science ( IF 44.7 ) Pub Date : 2020-03-16 , DOI: 10.1126/science.abb3221 Ruiyun Li 1 , Sen Pei 2 , Bin Chen 3 , Yimeng Song 4 , Tao Zhang 5 , Wan Yang 6 , Jeffrey Shaman 2
Affiliation
Undetected cases The virus causing coronavirus disease 2019 (COVID-19) has now become pandemic. How has it managed to spread from China to all around the world within 3 to 4 months? Li et al. used multiple sources to infer the proportion of early infections that went undetected and their contribution to virus spread. The researchers combined data from Tencent, one of the world's largest social media and technology companies, with a networked dynamic metapopulation model and Bayesian inference to analyze early spread within China. They estimate that ∼86% of cases were undocumented before travel restrictions were put in place. Before travel restriction and personal isolation were implemented, the transmission rate of undocumented infections was a little more than half that of the known cases. However, because of their greater numbers, undocumented infections were the source for ∼80% of the documented cases. Immediately after travel restrictions were imposed, ∼65% of cases were documented. These findings help to explain the lightning-fast spread of this virus around the world. Science, this issue p. 489 Prior to travel restrictions, most SARS-CoV-2 infections went undocumented and substantially contributed to global virus spread. Estimation of the prevalence and contagiousness of undocumented novel coronavirus [severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2)] infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here, we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model, and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV-2, including the fraction of undocumented infections and their contagiousness. We estimate that 86% of all infections were undocumented [95% credible interval (CI): 82–90%] before the 23 January 2020 travel restrictions. The transmission rate of undocumented infections per person was 55% the transmission rate of documented infections (95% CI: 46–62%), yet, because of their greater numbers, undocumented infections were the source of 79% of the documented cases. These findings explain the rapid geographic spread of SARS-CoV-2 and indicate that containment of this virus will be particularly challenging.
更新日期：2020-03-16
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