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本文引用的文献

1
Estimating the extent of asymptomatic COVID-19 and its potential for community transmission: Systematic review and meta-analysis.评估无症状新冠病毒感染的程度及其社区传播潜力:系统评价与荟萃分析。
J Assoc Med Microbiol Infect Dis Can. 2020 Dec 31;5(4):223-234. doi: 10.3138/jammi-2020-0030. eCollection 2020 Dec.
2
Revisiting the initial COVID-19 pandemic projections.重新审视最初对新冠疫情的预测。
Lancet Microbe. 2021 Mar;2(3):e91-e92. doi: 10.1016/S2666-5247(21)00029-X. Epub 2021 Mar 2.
3
An Outbreak of Covid-19 on an Aircraft Carrier.航空母舰上的新冠疫情爆发。
N Engl J Med. 2020 Dec 17;383(25):2417-2426. doi: 10.1056/NEJMoa2019375. Epub 2020 Nov 11.
4
Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: a modelling study using mobile phone data.利用手机数据的建模研究:中国深圳的人类流动限制对 COVID-19 传播的影响
Lancet Digit Health. 2020 Aug;2(8):e417-e424. doi: 10.1016/S2589-7500(20)30165-5. Epub 2020 Jul 27.
5
Physical distancing interventions and incidence of coronavirus disease 2019: natural experiment in 149 countries.物理隔离干预措施与 2019 年冠状病毒病发病率:149 个国家的自然实验。
BMJ. 2020 Jul 15;370:m2743. doi: 10.1136/bmj.m2743.
6
The implications of silent transmission for the control of COVID-19 outbreaks.静默传播对控制 COVID-19 疫情的影响。
Proc Natl Acad Sci U S A. 2020 Jul 28;117(30):17513-17515. doi: 10.1073/pnas.2008373117. Epub 2020 Jul 6.
7
Evidence for transmission of COVID-19 prior to symptom onset.有证据表明新冠病毒在症状出现之前就已经传播。
Elife. 2020 Jun 22;9:e57149. doi: 10.7554/eLife.57149.
8
Modeling Epidemics With Compartmental Models.用 compartments 模型对流行病进行建模。 注:你原文中的“compartmental”有误,应该是“compartmental”,正确的意思是“分区的、划分的” ,这里准确的译文应该是“用分区模型对流行病进行建模” 。
JAMA. 2020 Jun 23;323(24):2515-2516. doi: 10.1001/jama.2020.8420.
9
Screening of healthcare workers for SARS-CoV-2 highlights the role of asymptomatic carriage in COVID-19 transmission.对医护人员进行 SARS-CoV-2 筛查突出了无症状携带在 COVID-19 传播中的作用。
Elife. 2020 May 11;9:e58728. doi: 10.7554/eLife.58728.
10
Evidence Supporting Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 While Presymptomatic or Asymptomatic.支持严重急性呼吸综合征冠状病毒 2 在出现症状前或无症状时传播的证据。
Emerg Infect Dis. 2020 Jul;26(7). doi: 10.3201/eid2607.201595. Epub 2020 Jun 21.

无症状传播如何影响大流行期间的缓解和抑制策略。

How Asymptomatic Transmission Influences Mitigation and Suppression Strategies during a Pandemic.

机构信息

Force Medical Department, Naval Special Warfare Command, Coronado, CA, USA.

出版信息

Risk Anal. 2023 Apr;43(4):649-659. doi: 10.1111/risa.13736. Epub 2021 May 3.

DOI:10.1111/risa.13736
PMID:33938019
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8242667/
Abstract

Asymptomatic transmission complicates any public health strategies to combat a pandemic, which proved especially accurate in the case of COVID-19. Although asymptomatic cases are not unique to COVID-19, the high asymptomatic case rate raised many problems for developing effective public health interventions. The current modeling effort explored how asymptomatic transmission might impact pandemic responses in four key areas: isolation procedures, changes in reproduction rate, the potential for reduced transmission from asymptomatic cases, and social adherence to public health measures. A high rate of asymptomatic cases effectively requires large-scale public health suppression and mitigation procedures given that quarantine procedures alone could not prevent an outbreak for a virus such as SARS-CoV-2. This problem only becomes worse without lowering the effective reproduction rate, and even assuming the potential for reduced transmission, any virus with a high degree of asymptomatic transmission will likely produce a pandemic. Finally, there is a concern that asymptomatic individuals will also refuse to adhere to public health guidance. Analyses indicate that, given certain assumptions, even half of the population adhering to public health guidance could reduce the peak and flatten the curve by over 90%. Taken together, these analyses highlight the importance of taking asymptomatic cases into account when modeling viral spread and developing public health intervention strategies.

摘要

无症状传播使任何针对大流行的公共卫生策略变得复杂,这在 COVID-19 病例中被证明是特别准确的。尽管无症状病例并非 COVID-19 所独有,但高无症状病例率给制定有效的公共卫生干预措施带来了许多问题。目前的建模工作探讨了无症状传播如何在四个关键领域影响大流行应对措施:隔离程序、繁殖率的变化、无症状病例传播减少的可能性以及对公共卫生措施的社会遵守程度。鉴于仅隔离程序本身就无法预防 SARS-CoV-2 等病毒的爆发,高无症状病例率实际上需要大规模的公共卫生抑制和缓解措施。如果不降低有效繁殖率,这个问题只会变得更糟,即使假设传播减少的可能性,任何具有高度无症状传播的病毒都可能引发大流行。最后,人们担心无症状个体也会拒绝遵守公共卫生指导。分析表明,在某些假设下,即使有一半人口遵守公共卫生指导,也可以将峰值降低 90%以上并使曲线变平。总之,这些分析强调了在对病毒传播进行建模和制定公共卫生干预策略时考虑无症状病例的重要性。