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利用观察性数据量化旅行者对中国武汉 COVID-19 流行率估计的偏差。

Using observational data to quantify bias of traveller-derived COVID-19 prevalence estimates in Wuhan, China.

机构信息

Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA.

Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA.

出版信息

Lancet Infect Dis. 2020 Jul;20(7):803-808. doi: 10.1016/S1473-3099(20)30229-2. Epub 2020 Apr 1.

Abstract

BACKGROUND

The incidence of coronavirus disease 2019 (COVID-19) in Wuhan, China, has been estimated using imported case counts of international travellers, generally under the assumptions that all cases of the disease in travellers have been ascertained and that infection prevalence in travellers and residents is the same. However, findings indicate variation among locations in the capacity for detection of imported cases. Singapore has had very strong epidemiological surveillance and contact tracing capacity during previous infectious disease outbreaks and has consistently shown high sensitivity of case-detection during the COVID-19 outbreak.

METHODS

We used a Bayesian modelling approach to estimate the relative capacity for detection of imported cases of COVID-19 for 194 locations (excluding China) compared with that for Singapore. We also built a simple mathematical model of the point prevalence of infection in visitors to an epicentre relative to that in residents.

FINDINGS

The weighted global ability to detect Wuhan-to-location imported cases of COVID-19 was estimated to be 38% (95% highest posterior density interval [HPDI] 22-64) of Singapore's capacity. This value is equivalent to 2·8 (95% HPDI 1·5-4·4) times the current number of imported and reported cases that could have been detected if all locations had had the same detection capacity as Singapore. Using the second component of the Global Health Security index to stratify likely case-detection capacities, the ability to detect imported cases relative to Singapore was 40% (95% HPDI 22-67) among locations with high surveillance capacity, 37% (18-68) among locations with medium surveillance capacity, and 11% (0-42) among locations with low surveillance capacity. Treating all travellers as if they were residents (rather than accounting for the brief stay of some of these travellers in Wuhan) contributed modestly to underestimation of prevalence.

INTERPRETATION

Estimates of case counts in Wuhan based on assumptions of 100% detection in travellers could have been underestimated by several fold. Furthermore, severity estimates will be inflated several fold since they also rely on case count estimates. Finally, our model supports evidence that underdetected cases of COVID-19 have probably spread in most locations around the world, with greatest risk in locations of low detection capacity and high connectivity to the epicentre of the outbreak.

FUNDING

US National Institute of General Medical Sciences, and Fellowship Foundation Ramon Areces.

摘要

背景

中国武汉 2019 年冠状病毒病(COVID-19)的发病率是根据国际旅行者的输入病例数估算的,通常假设旅行者所有病例都已确定,并且旅行者和居民的感染流行率相同。但是,研究结果表明,在不同地点检测输入病例的能力存在差异。新加坡在以前的传染病爆发期间具有非常强大的流行病学监测和接触者追踪能力,并且在 COVID-19 爆发期间一直显示出很高的病例检出敏感性。

方法

我们使用贝叶斯建模方法来估计与新加坡相比,194 个地点(不包括中国)检测 COVID-19 输入病例的相对能力。我们还建立了一个简单的数学模型,用于计算疫区游客相对于居民的感染点流行率。

结果

估计全球检测武汉到地点的 COVID-19 输入病例的加权能力是新加坡能力的 38%(95%最高后验密度区间[HPDI] 22-64)。这一数值相当于如果所有地点都具有与新加坡相同的检测能力,则可以检测到的当前输入和报告病例数的 2.8 倍(95% HPDI 1.5-4.4)。使用全球卫生安全指数的第二部分对可能的病例检出能力进行分层,在高监测能力的地点,相对于新加坡的病例检出能力为 40%(95% HPDI 22-67),在中等监测能力的地点为 37%(18-68),在低监测能力的地点为 11%(0-42)。将所有旅行者视为居民(而不是考虑其中一些旅行者在武汉的短暂停留),这对流行率的低估略有贡献。

解释

基于旅行者 100%检出率的假设,对武汉病例数的估计可能被低估了几倍。此外,由于严重程度估计也依赖于病例数估计,因此严重程度估计也会被高估几倍。最后,我们的模型支持以下证据,即 COVID-19 未被检出的病例可能已在世界大多数地区传播,在检测能力低且与疫情中心连接度高的地区风险最大。

资金

美国国立普通医学科学研究所和拉蒙·阿雷塞斯奖学金基金会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3441/7270516/009c06715856/gr1_lrg.jpg

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