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通过隔离病例和接触者控制 COVID-19 疫情爆发的可行性。

Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts.

机构信息

Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

出版信息

Lancet Glob Health. 2020 Apr;8(4):e488-e496. doi: 10.1016/S2214-109X(20)30074-7. Epub 2020 Feb 28.

Abstract

BACKGROUND

Isolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19.

METHODS

We developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort.

FINDINGS

Simulated outbreaks starting with five initial cases, an R of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R of 2·5 more than 70% of contacts had to be traced, and for an R of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R was 1·5. For R values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset.

INTERPRETATION

In most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts.

FUNDING

Wellcome Trust, Global Challenges Research Fund, and Health Data Research UK.

摘要

背景

隔离病例和接触者追踪用于控制传染病的爆发,并且已经用于 2019 年冠状病毒病(COVID-19)。这种策略是否能够实现控制取决于病原体和应对措施的特点。在这里,我们使用数学模型来评估从 COVID-19 的输入病例中隔离和接触者追踪是否能够控制传播。

方法

我们开发了一个随机传播模型,根据 COVID-19 爆发进行了参数化。我们使用该模型来量化接触者追踪和隔离病例在控制类似于严重急性呼吸系统综合征冠状病毒 2(SARS-CoV-2)的病原体方面的潜在效果。我们考虑了不同初始病例数、基本繁殖数(R)、从症状出现到隔离的延迟、接触者被追踪的可能性、症状出现前发生传播的比例以及亚临床感染的比例的情况。我们假设隔离在模型中可以防止所有进一步的传播。如果在 12 周内或总病例数达到 5000 例之前结束传播,则认为疫情得到控制。我们使用隔离和接触者追踪来衡量控制疫情的效果,并量化每周追踪到的最大病例数,以衡量公共卫生工作的可行性。

发现

模拟从五个初始病例开始的疫情,R 值为 1.5,且症状出现前没有传播,即使接触者追踪的可能性较低,也可以得到控制;然而,当 R 值为 2.5 或 3.5 且症状出现前传播更多时,随着初始病例数的增加,控制疫情的可能性会降低。在不同的初始病例数情况下,当 R 值为 1.5 时,大多数情况下,只要不到 50%的接触者成功追踪,就可以控制疫情。为了控制大多数疫情,当 R 值为 2.5 时,超过 70%的接触者必须被追踪,当 R 值为 3.5 时,超过 90%的接触者必须被追踪。当 R 值为 1.5 时,症状出现与隔离之间的延迟在决定疫情是否可控方面起着最大的作用。对于 R 值为 2.5 或 3.5 的情况,如果有 40 个初始病例,只有在症状出现前发生的传播不到 1%的情况下,接触者追踪和隔离才具有潜在的可行性。

解释

在大多数情况下,高度有效的接触者追踪和病例隔离足以在 3 个月内控制 COVID-19 的新疫情。控制的可能性随着症状出现到隔离的延迟时间延长、接触者追踪确定的病例减少以及症状出现前传播增加而降低。该模型可以进行修改,以反映更新的传播特征和更具体的疫情控制定义,从而评估当地应对措施的潜在成功。

资助

惠康信托基金会、全球挑战研究基金和英国健康数据研究中心。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83b8/7158938/d32370930d79/gr1_lrg.jpg

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