Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe
Creators: name: Sharma, Mrinank
name: Mindermann, Sören
name: Rogers-Smith, Charlie
name: Leech, Gavin
name: Snodin, Benedict
name: Ahuja, Janvi
name: Sandbrink, Jonas B.
name: Monrad, Joshua Teperowski
name: Altman, George
name: Dhaliwal, Gurpreet
name: Finnveden, Lukas
name: Norman, Alexander John
name: Oehm, Sebastian B.
name: Sandkühler, Julia Fabienne
name: Aitchison, Laurence
name: Gavenčiak, Tomáš
name: Mellan, Thomas
name: Kulveit, Jan
name: Chindelevitch, Leonid
name: Flaxman, Seth
name: Gal, Yarin
name: Mishra, Swapnil
name: Bhatt, Samir
name: Brauner, Jan Markus
Publisher: name: Apollo - University of Cambridge Repository
Year: 2021
Subjects: Article
/692/700/478/174
/692/308/174
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article

description: Funder: European and Developing Countries Clinical Trials Partnership (EDCTP); doi: https://doi.org/10.13039/501100001713
description: Funder: MRC Centre for Global Infectious Disease Analysis (MR/R015600/1), jointly funded by the U.K. Medical Research Council (MRC) and the U.K. Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement. Community Jameel. The UK Research and Innovation (MR/V038109/1), the Academy of Medical Sciences Springboard Award (SBF004/1080), The MRC (MR/R015600/1), The BMGF (OPP1197730), Imperial College Healthcare NHS Trust- BRC Funding (RDA02), The Novo Nordisk Young Investigator Award (NNF20OC0059309) and The NIHR Health Protection Research Unit in Modelling Methodology. S. Bhatt thanks Microsoft AI for Health and Amazon AWS for computational credits.
description: Funder: EA Funds
description: Funder: University of Oxford (Oxford University); doi: https://doi.org/10.13039/501100000769
description: Funder: DeepMind
description: Funder: OpenPhilanthropy
description: Funder: UKRI Centre for Doctoral Training in Interactive Artificial Intelligence (EP/S022937/1)
description: Funder: Augustinus Fonden (Augustinus Foundation); doi: https://doi.org/10.13039/501100004954
description: Funder: Knud Højgaards Fond (Knud Højgaard Fund); doi: https://doi.org/10.13039/501100009938
description: Funder: Kai Lange og Gunhild Kai Langes Fond (Kai Lange and Gunhild Kai Lange Foundation); doi: https://doi.org/10.13039/501100008206
description: Funder: Aage og Johanne Louis-Hansens Fond (Aage and Johanne Louis-Hansen Foundation); doi: https://doi.org/10.13039/501100010344
description: Funder: William Demant Foundation
description: Funder: Boehringer Ingelheim Fonds (Stiftung für medizinische Grundlagenforschung); doi: https://doi.org/10.13039/501100001645
description: Funder: Imperial College COVID-19 Research Fund
description: Funder: Cancer Research UK (CRUK); doi: https://doi.org/10.13039/501100000289
description: Abstract: European governments use non-pharmaceutical interventions (NPIs) to control resurging waves of COVID-19. However, they only have outdated estimates for how effective individual NPIs were in the first wave. We estimate the effectiveness of 17 NPIs in Europe’s second wave from subnational case and death data by introducing a flexible hierarchical Bayesian transmission model and collecting the largest dataset of NPI implementation dates across Europe. Business closures, educational institution closures, and gathering bans reduced transmission, but reduced it less than they did in the first wave. This difference is likely due to organisational safety measures and individual protective behaviours—such as distancing—which made various areas of public life safer and thereby reduced the effect of closing them. Specifically, we find smaller effects for closing educational institutions, suggesting that stringent safety measures made schools safer compared to the first wave. Second-wave estimates outperform previous estimates at predicting transmission in Europe’s third wave.