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

Authors :
Flaxman, Seth
Mishra, Swapnil
Gandy, Axel
Unwin, H. Juliette T.
Mellan, Thomas A.
Coupland, Helen
Whittaker, Charles
Zhu, Harrison
Berah, Tresnia
Eaton, Jeffrey W.
Monod, Mélodie
Perez-Guzman, Pablo N.
Schmit, Nora
Cilloni, Lucia
Ainslie, Kylie E. C.
Baguelin, Marc
Boonyasiri, Adhiratha
Boyd, Olivia
Cattarino, Lorenzo
Cooper, Laura V.
Cucunubá, Zulma
Cuomo-Dannenburg, Gina
Dighe, Amy
Djaafara, Bimandra
Dorigatti, Ilaria
van Elsland, Sabine L.
FitzJohn, Richard G.
Gaythorpe, Katy A. M.
Geidelberg, Lily
Grassly, Nicholas C.
Green, William D.
Hallett, Timothy
Hamlet, Arran
Hinsley, Wes
Jeffrey, Ben
Knock, Edward
Laydon, Daniel J.
Nedjati-Gilani, Gemma
Nouvellet, Pierre
Parag, Kris V.
Siveroni, Igor
Thompson, Hayley A.
Verity, Robert
Volz, Erik
Walters, Caroline E.
Wang, Haowei
Wang, Yuanrong
Watson, Oliver J.
Winskill, Peter
Xi, Xiaoyue
Walker, Patrick G. T.
Ghani, Azra C.
Donnelly, Christl A.
Riley, Steven
Vollmer, Michaela A. C.
Ferguson, Neil M.
Okell, Lucy C.
Bhatt, Samir
Medical Research Council (MRC)
Wellcome Trust
Source :
Nature
Publication Year :
2020
Publisher :
Springer Science and Business Media LLC, 2020.

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

Details

ISSN :
14764687 and 00280836
Volume :
584
Database :
OpenAIRE
Journal :
Nature
Accession number :
edsair.doi.dedup.....fb0075f29c61d738f5667ed4260e31dd
Full Text :
https://doi.org/10.1038/s41586-020-2405-7