Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe

Seth Flaxman, Swapnil Mishra, Axel Gandy, H. Juliette T. Unwin, Thomas A. Mellan, Helen Coupland, Charles Whittaker, Harrison Zhu, Tresnia Berah, Jeffrey W. Eaton, Mélodie Monod, Azra C. Ghani, Christl A. Donnelly, Steven Riley, Michaela A. C. Vollmer, Neil M. Ferguson, Lucy C. Okell and Samir Bhatt ()
Additional contact information
Seth Flaxman: Imperial College London
Swapnil Mishra: Imperial College London
Axel Gandy: Imperial College London
H. Juliette T. Unwin: Imperial College London
Thomas A. Mellan: Imperial College London
Helen Coupland: Imperial College London
Charles Whittaker: Imperial College London
Harrison Zhu: Imperial College London
Tresnia Berah: Imperial College London
Jeffrey W. Eaton: Imperial College London
Mélodie Monod: Imperial College London
Azra C. Ghani: Imperial College London
Christl A. Donnelly: Imperial College London
Steven Riley: Imperial College London
Michaela A. C. Vollmer: Imperial College London
Neil M. Ferguson: Imperial College London
Lucy C. Okell: Imperial College London
Samir Bhatt: Imperial College London

Nature, 2020, vol. 584, issue 7820, 257-261

Abstract: Abstract Following the detection of the new coronavirus1 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics of coronavirus disease 2019 (COVID-19). In response, many European countries have implemented non-pharmaceutical interventions, such as the closure of schools and national lockdowns. Here we study the effect of major interventions across 11 European countries for the period from the start of the COVID-19 epidemics in February 2020 until 4 May 2020, when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks previously, allowing for the time lag between infection and death. We use partial pooling of information between countries, with both individual and shared effects on the time-varying reproduction number (Rt). Pooling allows for more information to be used, helps to overcome idiosyncrasies in the data and enables more-timely estimates. Our model relies on fixed estimates of some epidemiological parameters (such as the infection fatality rate), does not include importation or subnational variation and assumes that changes in Rt are an immediate response to interventions rather than gradual changes in behaviour. Amidst the ongoing pandemic, we rely on death data that are incomplete, show systematic biases in reporting and are subject to future consolidation. We estimate that—for all of the countries we consider here—current interventions have been sufficient to drive Rt below 1 (probability Rt

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (96)

Downloads: (external link)
https://www.nature.com/articles/s41586-020-2405-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:584:y:2020:i:7820:d:10.1038_s41586-020-2405-7

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-020-2405-7

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().