Arntzen Vera H, Nguyen Duc Manh, Fiocco Marta, Truong Thi Thanh Lan, Nguyen Hoai Thao Tam, Mai Thanh Buu, Nguyen Tu-Anh, Le Thanh Hoang Nhat, Choisy Marc, Phung Khanh Lam, Le Hong Nga, Geskus Ronald B
Mathematical Institute, Leiden University, Leiden, the Netherlands.
Centre for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam.
BMC Infect Dis. 2025 Apr 12;25(1):515. doi: 10.1186/s12879-025-10898-3.
The latency time (from infection to infectiousness) guides the choice of measures required to control an infectious disease. Estimates of the SARS-CoV- 2 latency time are sparse due to lack of appropriate and representative data. Infection time is rarely known exactly and exposure information may be subject to several biases. Information on the endpoint requires repeated testing. Moreover, estimation is challenging because both the starting point and endpoint are typically interval censored and data may be subject to length-biased sampling (truncation).
We collected detailed information on exposure from public health reports produced during an outbreak with the SARS-CoV- 2 Delta variant in Ho Chi Minh City, Vietnam, in May-July 2021. Using a custom digital form and application facilitated reliable choices on exposure window. This comprehensive data set on exposure and test results from 1951 individuals, collected in the absence of large-scale vaccination or earlier infection, is the first of its kind outside of China. We accounted for the doubly interval censored nature of the observations and went beyond the standard assumption of a constant infection risk over calendar time (exponential growth) and allowed for flexibility regarding the latency time (generalized gamma distribution). We addressed right truncation due to a cutoff in data collection and a finite quarantine length. Employing a Bayesian approach, using the program JAGS, made the analyses relatively straightforward.
Assuming exponential growth, our estimate of SARS-CoV- 2 Delta variant's mean latency time was 3.22 (95% Credible Interval 2.89 - 3.55) days; the median was 1.81 (95% CrI 1.44- 2.16) days; the 95 th percentile was 10.98 (95% CrI 9.91 - 12.41) days. These values were much larger if a uniform infection risk was assumed.
Using a Bayesian approach with the JAGS program, we were able to estimate the SARS-CoV- 2 latency time distribution of the Delta variant in infection-naive and vaccine-naive individuals. Estimates were sensitive to the assumptions made regarding the risk of infection within the exposure window. Compared to earlier studies, the median latency time was shorter, while the 95 th percentile was larger. Our results stress the importance of thoughtful data collection and analysis for evidence-based control of an infectious disease.
潜伏期(从感染到具有传染性的时间)指导着控制传染病所需措施的选择。由于缺乏合适且具有代表性的数据,对严重急性呼吸综合征冠状病毒2(SARS-CoV-2)潜伏期的估计很少。感染时间很少能确切知晓,且暴露信息可能存在多种偏差。关于终点的信息需要反复检测。此外,估计具有挑战性,因为起点和终点通常都是区间删失的,并且数据可能受到长度偏倚抽样(截断)的影响。
我们收集了2021年5月至7月越南胡志明市一起由SARS-CoV-2德尔塔变异株引发的疫情期间公共卫生报告中的详细暴露信息。使用定制的数字表格和应用程序有助于在暴露窗口方面做出可靠选择。这个关于1951名个体的暴露和检测结果的综合数据集是在中国境外同类数据集中首个在没有大规模疫苗接种或既往感染情况下收集的。我们考虑了观测值的双重区间删失性质,超越了日历时间内感染风险恒定(指数增长)的标准假设,并在潜伏期方面具有灵活性(广义伽马分布)。我们处理了由于数据收集截止和有限隔离期导致的右截断问题。采用贝叶斯方法,使用JAGS程序,使分析相对直接。
假设指数增长,我们对SARS-CoV-2德尔塔变异株平均潜伏期的估计为3.22天(95%可信区间2.89 - 3.55天);中位数为1.81天(95%可信区间1.44 - 2.16天);第95百分位数为10.98天(95%可信区间9.91 - 12.41天)。如果假设感染风险均匀,这些值会大得多。
使用带有JAGS程序的贝叶斯方法,我们能够估计未感染过且未接种过疫苗个体中SARS-CoV-2德尔塔变异株的潜伏期分布。估计值对暴露窗口内感染风险的假设很敏感。与早期研究相比,中位数潜伏期更短,而第95百分位数更大。我们的结果强调了为传染病的循证控制进行周全的数据收集和分析的重要性。