Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852, USA.
Beijing International Center for Mathematical Research, Peking University, 100871, China.
Sci Adv. 2020 Aug 14;6(33):eabc1202. doi: 10.1126/sciadv.abc1202. eCollection 2020 Aug.
We have proposed a novel, accurate low-cost method to estimate the incubation-period distribution of COVID-19 by conducting a cross-sectional and forward follow-up study. We identified those presymptomatic individuals at their time of departure from Wuhan and followed them until the development of symptoms. The renewal process was adopted by considering the incubation period as a renewal and the duration between departure and symptoms onset as a forward time. Such a method enhances the accuracy of estimation by reducing recall bias and using the readily available data. The estimated median incubation period was 7.76 days [95% confidence interval (CI): 7.02 to 8.53], and the 90th percentile was 14.28 days (95% CI: 13.64 to 14.90). By including the possibility that a small portion of patients may contract the disease on their way out of Wuhan, the estimated probability that the incubation period is longer than 14 days was between 5 and 10%.
我们提出了一种新颖、准确且低成本的方法，通过进行横断面和前瞻性随访研究来估计 COVID-19 的潜伏期分布。我们在武汉出发时识别出那些有症状前的个体，并跟踪他们，直到出现症状。通过将潜伏期视为更新，将离开武汉与出现症状之间的时间视为向前时间，采用更新过程。这种方法通过减少回忆偏倚和使用现成的数据来提高估计的准确性。估计的中位潜伏期为 7.76 天（95%置信区间：7.02 至 8.53），第 90 百分位数为 14.28 天（95%置信区间：13.64 至 14.90）。通过考虑一小部分患者可能在离开武汉的途中感染疾病的可能性，潜伏期超过 14 天的估计概率在 5%至 10%之间。
