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 However, they only have outdated estimates for how effective individual NPIs were in the first wave.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.

Abdul Latif Jameel Institute for Disease and Emergency Analytics School of Public Health Imperial College London London UK

Department of Computer Science University of Bristol Bristol UK

Department of Engineering Science University of Oxford Oxford UK

Department of Health Policy London School of Economics and Political Science London UK

Department of Mathematics Imperial College London London UK

Department of Statistics University of Oxford Oxford UK

Faculty of Public Health and Policy London School of Hygiene and Tropical Medicine London UK

Future of Humanity Institute University of Oxford Oxford UK

Independent scholar Prague Czech Republic

Manchester University NHS Foundation Trust Manchester UK

Mathematical Physical and Life Sciences Doctoral Training Centre University of Oxford Oxford UK

Medical Research Council Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London UK

Medical Research Council Laboratory of Molecular Biology Cambridge UK

Medical Sciences Division University of Oxford Oxford UK

OATML Group Department of Computer Science University of Oxford Oxford UK

Oxford Applied and Theoretical Machine Learning Group Department of Computer Science University of Oxford Oxford UK

School of Life Sciences University of Warwick Coventry UK

Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark

The Francis Crick Institute London UK

University of Cambridge Cambridge UK

University of Essen Essen Germany

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An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors

Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions

Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe