02248nam a22003257a 4500001001300000003000400013005001700017008004100034245013800075520109900213580011301312650001801425650002001443650003801463650004101501650003101542650001101573653001301584700001601597700001501613700001501628700001901643700001701662700001501679700002101694856005801715942000701773999001501780952012701795D0001C000066DOH20201123132702.0201123b           ||||| |||| 00| 0 eng d00aSubstantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2) /cRuiyun Li [and six others]3 aEstimation 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.  aIn: Science. 2020 vol.368 (6490) page: 489–493.
Published online 2020 Mar 16. doi: 10.1126/science.abb3221 2aBayes Theorem 2aBetacoronavirus 2aCoronavirus Infectionsxdiagnosis 2aCoronavirus Infectionsxtransmission 2aEpidemiological Monitoring 2aTravel  aCOVID-191 aLi, Ruiyun 1 aPei, Sen  1 aChen, Bin 1 aSong, Yimeng  1 aZhang, Tao  1 aYang, Wan 1 aShaman, Jeffrey   uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7164387/  cCV  c1803d1803  00104070aDOHCLbDOHCLcElectronicResd2020-11-23l0oCOVID-19-000066pD0001C000066r2020-11-23 00:00:00w2020-11-23yCV