Inferring the effectiveness of government interventions against COVID-19

Jan M Brauner; Sören Mindermann; Mrinank Sharma; David Johnston; John Salvatier; Tomáš Gavenčiak; Anna B Stephenson; Gavin Leech; George Altman; Vladimir Mikulik; Alexander John Norman; Joshua Teperowski Monrad; Tamay Besiroglu; Hong Ge; Meghan A Hartwick; Yee Whye Teh; Leonid Chindelevitch; Yarin Gal; Jan Kulveit

doi:10.1126/science.abd9338

Inferring the effectiveness of government interventions against COVID-19

Science. 2021 Feb 19;371(6531):eabd9338. doi: 10.1126/science.abd9338. Epub 2020 Dec 15.

Authors

Jan M Brauner^#^{1

2}, Sören Mindermann^#¹, Mrinank Sharma^#^{3

4

5}, David Johnston^{6

7}, John Salvatier⁷, Tomáš Gavenčiak⁸, Anna B Stephenson⁹, Gavin Leech¹⁰, George Altman¹¹, Vladimir Mikulik¹², Alexander John Norman¹³, Joshua Teperowski Monrad^{2

14

15}, Tamay Besiroglu¹⁶, Hong Ge¹⁷, Meghan A Hartwick¹⁸, Yee Whye Teh⁴, Leonid Chindelevitch^#^{19

20}, Yarin Gal^#²¹, Jan Kulveit^#²

Affiliations

¹ Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, Oxford, UK. jan.brauner@cs.ox.ac.uk soren.mindermann@cs.ox.ac.uk mrinank@robots.ox.ac.uk.
² Future of Humanity Institute, University of Oxford, Oxford, UK.
³ Future of Humanity Institute, University of Oxford, Oxford, UK. jan.brauner@cs.ox.ac.uk soren.mindermann@cs.ox.ac.uk mrinank@robots.ox.ac.uk.
⁴ Department of Statistics, University of Oxford, Oxford, UK.
⁵ Department of Engineering Science, University of Oxford, Oxford, UK.
⁶ College of Engineering and Computer Science, Australian National University, Canberra, Australia.
⁷ Quantified Uncertainty Research Institute, San Francisco, CA, USA.
⁸ Independent scholar, Prague, Czech Republic.
⁹ Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
¹⁰ School of Computer Science, University of Bristol, Bristol, UK.
¹¹ School of Medical Sciences, University of Manchester, Manchester, UK.
¹² Independent scholar, London, UK.
¹³ Mathematical, Physical and Life Sciences (MPLS) Doctoral Training Centre, University of Oxford, Oxford, UK.
¹⁴ Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK.
¹⁵ Department of Health Policy, London School of Economics and Political Science, London, UK.
¹⁶ Faculty of Economics, University of Cambridge, Cambridge, UK.
¹⁷ Engineering Department, University of Cambridge, Cambridge, UK.
¹⁸ Tufts Initiative for the Forecasting and Modeling of Infectious Diseases, Tufts University, Boston, MA, USA.
¹⁹ Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.
²⁰ Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, UK.
²¹ Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, Oxford, UK.

^# Contributed equally.

Abstract

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 non-European countries between January and the end of May 2020. We estimated the effectiveness of these NPIs, which range from limiting gathering sizes and closing businesses or 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.

MeSH terms

Asia / epidemiology
Bayes Theorem
COVID-19 / prevention & control*
COVID-19 / transmission
Commerce
Communicable Disease Control*
Europe / epidemiology
Government*
Health Policy
Humans
Models, Theoretical
Pandemics / prevention & control
Physical Distancing
Schools
Universities

Abstract

MeSH terms

Grants and funding