BETS: THE DANGERS OF SELECTION BIAS IN EARLY ANALYSES OF THE CORONAVIRUS DISEASE (COVID-19) PANDEMIC

• 成果类型: Article
• 署名作者: Zhao, Qingyuan; Ju, Nianqiao; Bacallado, Sergio; Shah, Rajen D.
• 署名单位: University of Cambridge; Harvard University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1401
• 发表日期: 2021
• 页码: 363-390

摘要

The coronavirus disease 2019 (COVID-19) has quickly grown from a regional outbreak in Wuhan, China, to a global pandemic. Early estimates of the epidemic growth and incubation period of COVID-19 may have been biased due to sample selection. Using detailed case reports from 14 locations in and outside mainland China, we obtained 378 Wuhan-exported cases who left Wuhan before an abrupt travel quarantine. We developed a generative model we call BETS for four key epidemiological events-Beginning of exposure, End of exposure, time of Transmission, and time of Symptom onset (BETS)-and derived explicit formulas to correct for the sample selection. We gave a detailed illustration of why some early and highly influential analyses of the COVID-19 pandemic were severely biased. All our analyses, regardless of which subsample and model were being used, point to an epidemic doubling time of two to 2.5 days during the early outbreak in Wuhan. A Bayesian nonparametric analysis further suggests that about 5% of the symptomatic cases may not develop symptoms within 14 days of infection and that men may be much more likely than women to develop symptoms within two days of infection.