IRUS Total

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

File Description SizeFormat 
s41467-021-26013-4.pdfPublished version4.04 MBAdobe PDFView/Open
Title: Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe
Authors: Bhatt, S
Item Type: Journal Article
Abstract: As European governments face resurging waves of COVID-19, non-pharmaceutical interventions (NPIs) continue to be the primary tool for infection control. However, updated estimates of their relative effectiveness have been absent for Europe’s second wave, largely due to a lack of collated data that considers the increased subnational variation and diversity of NPIs. We collect the largest dataset of NPI implementation dates in Europe, spanning 114 subnational areas in 7 countries, with a systematic categorisation of interventions tailored to the second wave. Using a hierarchical Bayesian transmission model, we estimate the effectiveness of 17 NPIs from local case and death data. We manually validate the data, address limitations in modelling from previous studies, and extensively test the robustness of our estimates. The combined effect of all NPIs was smaller relative to estimates from the first half of 2020, indicating the strong influence of safety measures and individual protective behaviours--such as distancing--that persisted after the first wave. Closing specific businesses was highly effective. Gathering restrictions were highly effective but only for the strictest limits. We find smaller effects for closing educational institutions compared to the first wave, suggesting that safer operation of schools was possible with a set of stringent safety measures including testing and tracing, preventing mixing, and smaller classes. These results underscore that effectiveness estimates from the early stage of an epidemic are measured relative to pre-pandemic behaviour. Updated estimates are required to inform policy in an ongoing pandemic.
Issue Date: 5-Oct-2021
Date of Acceptance: 23-Aug-2021
URI: http://hdl.handle.net/10044/1/91607
DOI: 10.1038/s41467-021-26013-4
ISSN: 2041-1723
Publisher: Nature Research
Start Page: 1
End Page: 12
Journal / Book Title: Nature Communications
Volume: 12
Copyright Statement: © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Sponsor/Funder: Medical Research Council (MRC)
Medical Research Council (MRC)
Imperial College Healthcare NHS Trust- BRC Funding
The Academy of Medical Sciences
National Institute for Health Research
UK Research and Innovation
Funder's Grant Number: MR/K010174/1B
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
Basic Reproduction Number
Models, Theoretical
Time Factors
Publication Status: Published
Online Publication Date: 2021-10-05
Appears in Collections:Faculty of Medicine
Imperial College London COVID-19
School of Public Health

This item is licensed under a Creative Commons License Creative Commons