论文标题
赌注：选择冠状病毒疾病（COVID-19）大流行中选择偏见的危险
BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic
论文作者
论文摘要
2019年冠状病毒病（Covid-19）迅速从中国武汉的一次区域爆发到全球大流行。由于样本选择，可能会偏向于COVID-19的流行病生长和孵育期的早期估计。利用来自中国大陆14个地点的详细案例报告，我们获得了378个武汉外出的案件，这些案件在突然旅行隔离之前离开了武汉。我们开发了一个生成模型，我们称之为四个关键流行病学事件的赌注 - 接触的开始，暴露终结，传播时间和症状发作时间（BETS） - 以及派生的显式公式以校正样品选择。我们给出了一个详细的说明，说明了为什么对19009年大流行的一些早期和高度影响力分析被严重偏见。无论使用哪种子样本和模型，我们的所有分析都表明，在武汉的早期暴发期间，流行病的倍增时间为2至2.5天。贝叶斯非参数分析进一步表明，大约5％的有症状病例可能不会在感染后的14天内出现症状，并且男性在感染后的2天内可能比女性更有可能出现症状。
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 2 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 2 days of infection.
