评 Ferguson 等人的“减少 COVID-19 死亡率和医疗需求的非药物干预（NPIs）的影响”一文。
Commentary on Ferguson, et al., "Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand".
机构信息
Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, USA.
Department of Public Health Sciences, University of Virginia, Charlottesville, USA.
出版信息
Bull Math Biol. 2020 Apr 8;82(4):52. doi: 10.1007/s11538-020-00726-x.
Abstract
A recent manuscript (Ferguson et al. in Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand, Imperial College COVID-19 Response Team, London, 2020. https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf) from Imperial College modelers examining ways to mitigate and control the spread of COVID-19 has attracted much attention. In this paper, we will discuss a coarse taxonomy of models and explore the context and significance of the Imperial College and other models in contributing to the analysis of COVID-19.
摘要
一篇最近的论文（Ferguson 等人，《非药物干预（NPIs）对降低 COVID-19 死亡率和医疗需求的影响》，帝国理工学院 COVID-19 应对小组，伦敦，2020 年。https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf），来自帝国理工学院的建模人员，研究了减轻和控制 COVID-19 传播的方法，引起了广泛关注。在本文中，我们将讨论模型的粗分类法，并探讨帝国理工学院和其他模型在分析 COVID-19 方面的背景和意义。
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本文引用的文献
1
Opinion: Mathematical models: a key tool for outbreak response.观点：数学模型：疫情应对的关键工具。
Proc Natl Acad Sci U S A. 2014 Dec 23;111(51):18095-6. doi: 10.1073/pnas.1421551111. Epub 2014 Dec 10.
2
Theodore E. Woodward award: non-pharmaceutical interventions employed by major American cities during the 1918-19 influenza pandemic.西奥多·E·伍德沃德奖：美国主要城市在1918 - 19年流感大流行期间采用的非药物干预措施
Trans Am Clin Climatol Assoc. 2008;119:129-38; discussion 138-42.
3
Modeling targeted layered containment of an influenza pandemic in the United States.美国流感大流行的目标分层防控建模
Proc Natl Acad Sci U S A. 2008 Mar 25;105(12):4639-44. doi: 10.1073/pnas.0706849105. Epub 2008 Mar 10.
4
Public health interventions and epidemic intensity during the 1918 influenza pandemic.1918年流感大流行期间的公共卫生干预措施与疫情强度
Proc Natl Acad Sci U S A. 2007 May 1;104(18):7582-7. doi: 10.1073/pnas.0610941104. Epub 2007 Apr 6.
5
Strategies for containing an emerging influenza pandemic in Southeast Asia.东南亚遏制新型流感大流行的策略。
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6
Containing pandemic influenza at the source.从源头控制大流行性流感。
Science. 2005 Aug 12;309(5737):1083-7. doi: 10.1126/science.1115717. Epub 2005 Aug 3.
7
Modelling disease outbreaks in realistic urban social networks.在现实城市社交网络中对疾病爆发进行建模。
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8
Planning for smallpox outbreaks.天花疫情应对规划。
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9
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10
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