Inferring the effectiveness of government interventions against COVID-19,Science

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Inferring the effectiveness of government interventions against COVID-19
Science ( IF 44.7 ) Pub Date : 2020-12-15 , DOI: 10.1126/science.abd9338
Jan M Brauner _{1,

2} , Sören Mindermann ₁ , Mrinank Sharma _{2,

3,

4} , David Johnston _{5,

6} , John Salvatier ₆ , Tomáš Gavenčiak ₇ , Anna B Stephenson ₈ , Gavin Leech ₉ , George Altman ₁₀ , Vladimir Mikulik ₁₁ , Alexander John Norman ₁₂ , Joshua Teperowski Monrad _{2,

13,

14} , Tamay Besiroglu ₁₅ , Hong Ge ₁₆ , Meghan A Hartwick ₁₇ , Yee Whye Teh ₃ , Leonid Chindelevitch _{18,

19} , Yarin Gal ₁ , Jan Kulveit ₂

Affiliation

Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, Oxford, UK.
Future of Humanity Institute, University of Oxford, Oxford, UK.
Department of Statistics, University of Oxford, Oxford, UK.
Department of Engineering Science, University of Oxford, Oxford, UK.
College of Engineering and Computer Science, Australian National University, Canberra, Australia.
Quantified Uncertainty Research Institute, San Francisco, CA, USA.
Independent scholar, Prague, Czech Republic.
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
School of Computer Science, University of Bristol, Bristol, UK.
School of Medical Sciences, University of Manchester, Manchester, UK.
Independent scholar, London, UK.
Mathematical, Physical and Life Sciences (MPLS) Doctoral Training Centre, University of Oxford, Oxford, UK.
Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK.
Department of Health Policy, London School of Economics and Political Science, London, UK.
Faculty of Economics, University of Cambridge, Cambridge, UK.
Engineering Department, University of Cambridge, Cambridge, UK.
Tufts Initiative for the Forecasting and Modeling of Infectious Diseases, Tufts University, Boston, MA, USA.
Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.
Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK.

Governments are attempting to control the COVID-19 pandemic with nonpharmaceutical interventions (NPIs). However, the effectiveness of different NPIs at reducing transmission is poorly understood. We gathered chronological data on the implementation of NPIs for several European, and other, countries between January and the end of May 2020. We estimate the effectiveness of NPIs, ranging from limiting gathering sizes, business closures, and closure of educational institutions to stay-at-home orders. To do so, we used a Bayesian hierarchical model that links NPI implementation dates to national case and death counts and supported the results with extensive empirical validation. Closing all educational institutions, limiting gatherings to 10 people or less, and closing face-to-face businesses each reduced transmission considerably. The additional effect of stay-at-home orders was comparatively small.

中文翻译：

推断政府针对 COVID-19 干预措施的有效性

各国政府正在尝试通过非药物干预措施 (NPI) 来控制 COVID-19 大流行。然而，人们对不同非营利机构在减少传播方面的有效性知之甚少。我们收集了 2020 年 1 月至 5 月底期间几个欧洲和其他国家/地区 NPI 实施情况的时间数据。我们估计了 NPI 的有效性，包括限制聚会规模、关闭企业和关闭教育机构，以及保留-在家订单。为此，我们使用贝叶斯分层模型，将 NPI 实施日期与国家病例和死亡计数联系起来，并通过广泛的实证验证支持结果。关闭所有教育机构、将聚会人数限制在 10 人或以下以及关闭面对面的企业都大大减少了传播。居家令的额外影响相对较小。

更新日期：2020-12-15

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