Khadem Charvadeh Yasin, Yi Grace Y, Bian Yuan, He Wenqing
Department of Statistical and Actuarial Sciences, University of Western Ontario, London, ON Canada.
Department of Computer Science, University of Western Ontario, London, ON Canada.
Stat Biosci. 2022;14(1):175-190. doi: 10.1007/s12561-021-09320-8. Epub 2021 Sep 9.
To confine the spread of an infectious disease, setting a sensible quarantine time is crucial. To this end, it is imperative to well understand the distribution of incubation times of the disease. Regarding the ongoing COVID-19 pandemic, 14-days is commonly taken as a quarantine time to curb the virus spread in balancing the impacts of COVID-19 on diverse aspects of the society, including public health, economy, and humanity perspectives, etc. However, setting a sensible quarantine time is not trivial and it depends on various underlying factors. In this article, we take an angle of examining the distribution of the COVID-19 incubation time using likelihood-based methods. Our study is carried out on a dataset of 178 COVID-19 cases dated from January 20, 2020 to February 29, 2020, with the information of exposure periods and dates of symptom onset collected. To gain a good understanding of possible scenarios, we employ different models to describe incubation times of COVID-19. Our findings suggest that statistically, the 14-day quarantine time may not be long enough to control the probability of an early release of infected individuals to be small. While the size of the study data is not large enough to offer us a definitely acceptable quarantine time, and further in practice, the decision-makers may take account of other factors related to social and economic concerns to set up a practically acceptable quarantine time, our study demonstrates useful methods to determine a reasonable quarantine time from a statistical standpoint. Further, it reveals some associated complexity for fully understanding the COVID-19 incubation time distribution.
The online version contains supplementary material available at 10.1007/s12561-021-09320-8.
为了控制传染病的传播，设定合理的隔离时间至关重要。为此，必须充分了解该疾病潜伏期的分布情况。对于当前的新冠疫情，14天通常被用作隔离时间，以在平衡新冠疫情对社会各个方面（包括公共卫生、经济和人文视角等）的影响的同时遏制病毒传播。然而，设定合理的隔离时间并非易事，它取决于各种潜在因素。在本文中，我们从使用基于似然性的方法研究新冠病毒潜伏期分布的角度展开探讨。我们的研究基于一个包含178例新冠病例的数据集进行，这些病例的日期从2020年1月20日至2020年2月29日，收集了暴露期和症状出现日期的信息。为了深入了解可能的情况，我们采用不同模型来描述新冠病毒的潜伏期。我们的研究结果表明，从统计学角度来看，14天的隔离时间可能不足以将感染个体提前释放的概率控制在较低水平。虽然研究数据的规模不足以让我们确定一个绝对可接受的隔离时间，而且在实际操作中，决策者可能会考虑与社会和经济问题相关的其他因素来设定一个实际可接受的隔离时间，但我们的研究展示了从统计学角度确定合理隔离时间的有用方法。此外，它揭示了全面了解新冠病毒潜伏期分布存在的一些相关复杂性。
在线版本包含可在10.1007/s12561-021-09320-8获取的补充材料。
