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Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020.

基于症状发作数据估计冠状病毒疾病 (新型冠状病毒肺炎) 的产生间隔,2020 年 3 月。

  • 影响因子:5.05
  • DOI:10.2807/1560-7917.ES.2020.25.17.2000257
  • 作者列表:"Ganyani T","Kremer C","Chen D","Torneri A","Faes C","Wallinga J","Hens N
  • 发表时间:2020-04-01
Abstract

: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.

摘要

研究背景冠状病毒疾病 (新型冠状病毒肺炎) 爆发的关键传染病参数对建模研究和指导干预策略至关重要。aimWe估计新型冠状病毒肺炎的产生间隔、序列间隔、症状前传播的比例和有效繁殖数。我们说明基于序列间隔估计计算的再现数可能有偏差。方法我们使用来自中国新加坡和天津集群的暴发数据,从症状发作数据中估计产生间隔,同时承认潜伏期分布和潜在传输网络的不确定性。从这些估计中,我们获得了序列间隔、症状前传播的比例和繁殖数。结果新加坡的平均世代间隔为 5.20 天 (95% 可信间隔 (CrI): 3.78-6.78),天津的平均世代间隔为 3.95 天 (95% CrI: 3.01-4.91)。症状前传播的比例新加坡为 48% (95% CrI: 32-67),天津为 62% (95% CrI: 50-76)。基于生成间隔分布的再现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,从疫情数据中估计这些数量需要详细的接触者追踪信息。结论高估计症状前传播的比例意味着病例发现和接触者追踪需要辅以物理距离措施,以控制新型冠状病毒肺炎暴发。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,这可能会增加有症状前个体的感染比例。

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翻译标题与摘要 下载文献
影响因子:2.48
发表时间:2020-04-01
来源期刊:Infection
DOI:10.1007/s15010-020-01401-y
作者列表:["Cheng ZJ","Shan J"]

METHODS::There is a current worldwide outbreak of a new type of coronavirus (2019-nCoV), which originated from Wuhan in China and has now spread to 17 other countries. Governments are under increased pressure to stop the outbreak spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak sites and from laboratories supporting the investigation. This paper aggregates and consolidates the virology, epidemiology, clinical management strategies from both English and Chinese literature, official news channels, and other official government documents. In addition, by fitting the number of infections with a single-term exponential model, we report that the infection is spreading at an exponential rate, with a doubling period of 1.8 days.

呼吸道感染方向

呼吸道感染分为上呼吸道感染与下呼吸道感染。上呼吸道感染是指自鼻腔至喉部之间的急性炎症的总称,是最常见的感染性疾病。下呼吸道感染是最常见的感染性疾患,治疗时必须明确引起感染的病原体以选择有效的抗生素。

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