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 However, they only have outdated estimates for how effective individual NPIs were in the first wave.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.
| Original language | English |
|---|---|
| Article number | 5820 |
| Journal | Nature Communications |
| Volume | 12 |
| Issue number | 1 |
| Early online date | 5 Oct 2021 |
| DOIs | |
| Publication status | Published - Dec 2021 |
Bibliographical note
Funding Information:J. Kulveit has advised several governmental and nongovernmental entities about interventions against COVID-19. L. Chindelevitch has acted as a paid consultant to Pfizer and the Foundation for Innovative New Diagnostics, outside of the submitted work. He also volunteers as a scientist with the creative destruction lab Oxford. Y. Gal has received a research grant (studentship) from GlaxoSmithKline, outside of the submitted work. S. Bhatt sits on and advises the Scientific Pandemic Influenza Group on Modelling (SPI-M) a subgroup of the Scientific Advisory Group for Emergencies (SAGE). His work on this board is funded by the UKRI/MRC. The remaining authors declare no competing interests. None of the above-mentioned entities had any influence on the conceptualisation, design, data collection, analysis, decision to publish, or preparation of the paper.
Funding Information:
M. Sharma was supported by the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems (EP/S024050/1) and a grant from the EA Funds programme. S. Mindermann’s funding for graduate studies was from Oxford University and DeepMind. C. Rogers-Smith was supported by a grant from Open Philanthropy. A.J. Norman was supported by the U.K. BBSRC [grant number BB/T008784/1] and Open Philanthropy. J. Ahuja was supported by Open Philanthropy. J.T. Monrad was supported by the Augustinus Foundation, the Knud Højgaard Foundation, the William Demant Foundation, the Kai Lange and Gunhild Kai Lange Foundation, and the Aage and Johanne Louis-Hansen Foundation. G.Leech was supported by the UKRI Centre for Doctoral Training in Interactive Artificial Intelligence (EP/S022937/1). S.B. Oehm was supported by the Boehringer Ingelheim Fonds. L. Chindelevitch and S. Bhatt acknowledge funding from the 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; are part of the EDCTP2 programme supported by the European Union; and acknowledge funding by Community Jameel. S. Flaxman acknowledges the EPSRC (EP/V002910/1) and the Imperial College COVID-19 Research Fund. J.M. Brauner was supported by the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems (EP/S024050/1) and by Cancer Research UK. S. Bhatt acknowledges 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.
Publisher Copyright:
© 2021, The Author(s).
Research Groups and Themes
- Interactive Artificial Intelligence CDT