I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium.
Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands.
Euro Surveill. 2020 Apr;25(17). doi: 10.2807/1560-7917.ES.2020.25.17.2000257.
BackgroundEstimating key infectious disease parameters from the coronavirus disease (COVID-19) outbreak is essential for modelling studies and guiding intervention strategies.AimWe estimate the generation interval, serial interval, proportion of pre-symptomatic transmission and effective reproduction number of COVID-19. We illustrate that reproduction numbers calculated based on serial interval estimates can be biased.MethodsWe used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates, we obtained the serial interval, proportions of pre-symptomatic transmission and reproduction numbers.ResultsThe mean generation interval was 5.20 days (95% credible interval (CrI): 3.78-6.78) for Singapore and 3.95 days (95% CrI: 3.01-4.91) for Tianjin. The proportion of pre-symptomatic transmission was 48% (95% CrI: 32-67) for Singapore and 62% (95% CrI: 50-76) for Tianjin. Reproduction number estimates based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Sensitivity analyses showed that estimating these quantities from outbreak data requires detailed contact tracing information.ConclusionHigh estimates of the proportion of pre-symptomatic transmission imply that case finding and contact tracing need to be supplemented by physical distancing measures in order to control the COVID-19 outbreak. Notably, quarantine and other containment measures were already in place at the time of data collection, which may inflate the proportion of infections from pre-symptomatic individuals.
背景
从冠状病毒病(COVID-19)爆发中估计关键传染病参数对于模型研究和指导干预策略至关重要。
目的
我们估计 COVID-19 的代际间隔、序列间隔、无症状传播比例和有效繁殖数。我们说明基于序列间隔估计计算的繁殖数可能存在偏差。
方法
我们使用新加坡和中国天津集群的暴发数据,从症状出现数据中估计代际间隔,同时承认潜伏期分布和潜在传播网络的不确定性。从这些估计中,我们获得了序列间隔、无症状传播比例和繁殖数。
结果
新加坡的平均代际间隔为 5.20 天(95%可信区间(CrI):3.78-6.78),天津为 3.95 天(95% CrI:3.01-4.91)。新加坡无症状传播的比例为 48%(95% CrI:32-67),天津为 62%(95% CrI:50-76)。基于代际间隔分布的繁殖数估计值略高于基于序列间隔分布的估计值。敏感性分析表明,从暴发数据中估计这些数量需要详细的接触追踪信息。
结论
无症状传播比例的高估计意味着病例发现和接触追踪需要辅以物理距离措施,以控制 COVID-19 的爆发。值得注意的是,在数据收集时,检疫和其他遏制措施已经到位,这可能会夸大来自无症状个体的感染比例。