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Epidemiological characteristics of the first 53 laboratory-confirmed cases of COVID-19 epidemic in Hong Kong, 13 February 2020

机译:2020年2月13日香港第一个53实验室证实案例的流行病学特征

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Background COVID-19, caused by SARS-CoV-2, first appeared in China and subsequently developed into an ongoing epidemic. Understanding epidemiological factors characterising the transmission dynamics of this disease is of fundamental importance. Aims This study aimed to describe key epidemiological parameters of COVID-19 in Hong Kong. Methods We extracted data of confirmed COVID-19 cases and their close contacts from the publicly available information released by the Hong Kong Centre for Health Protection. We used doubly interval censored likelihood to estimate containment delay and serial interval, by fitting gamma, lognormal and Weibull distributions to respective empirical values using Bayesian framework with right truncation. A generalised linear regression model was employed to identify factors associated with containment delay. Secondary attack rate was also estimated. Results The empirical containment delay was 6.39?days; whereas after adjusting for right truncation with the best-fit Weibull distribution, it was 10.4?days (95%?CrI:?7.15 to 19.81). Containment delay increased significantly over time. Local source of infection and number of doctor consultations before isolation were associated with longer containment delay. The empirical serial interval was 4.58–6.06?days; whereas the best-fit lognormal distribution to 26 certain-and-probable infector–infectee paired data gave an estimate of 4.77?days (95%?CrI:?3.47 to 6.90) with right-truncation. The secondary attack rate among close contacts was 11.7%. Conclusion With a considerable containment delay and short serial interval, contact-tracing effectiveness may not be optimised to halt the transmission with rapid generations replacement. Our study highlights the transmission risk of social interaction and pivotal role of physical distancing in suppressing the epidemic.
机译:背景Covid-19由SARS-COV-2引起的,首先出现在中国,随后发展成为持续的流行病。了解表征这种疾病的传导动态的流行病学因素具有重要意义。目的这项研究旨在描述香港Covid-19的关键流行病学参数。方法我们提取了确认的Covid-19案件的数据以及香港健康中心公布的公开信息的密切联系。我们使用双级间隔缩短的可能性来估算伽马,逻辑和Weibull分布到使用正确截断的贝叶斯框架的伽马,逻辑和Weibull分布来估算容纳延迟和串行间隔。采用广义线性回归模型来识别与遏制延迟相关的因素。还估计了二次攻击率。结果经验遏制延迟为6.39?天;虽然用最佳拟合威布尔分布调整右截断后,但它是10.4天(95%?CRI:?7.15至19.81)。容纳延迟随着时间的推移而显着增加。在隔离前的当地感染源和医生咨询数量与更长的遏制延迟相关。经验序列间隔为4.58-6.06?天;虽然最佳的Lognormal分布到26个某种且可能的感染者 - 感染者配对数据,但估计为4.77?天(95%?CRI:?3.47至6.90),具有右截断。密闭接触之间的二次攻击率为11.7%。结论采用相当大的容纳延迟和短串行间隔,可能不优化接触追踪效果,以便利用快速代替换乘来停止传输。我们的研究凸显了社会互动的传输风险,在抑制流行病方面的身体偏差的枢轴作用。

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