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How did governmental interventions affect the spread of COVID-19 in European countries?
BACKGROUND: To reduce the transmission of the severe acute respiratory syndrome coronavirus 2 in its first wave, European governments have implemented successive measures to encourage social distancing. However, it remained unclear how effectively measures reduced the spread of the virus. We examine...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7908011/ https://www.ncbi.nlm.nih.gov/pubmed/33637062 http://dx.doi.org/10.1186/s12889-021-10257-2 |
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author | Post, Richard A. J. Regis, Marta Zhan, Zhuozhao van den Heuvel, Edwin R. |
author_facet | Post, Richard A. J. Regis, Marta Zhan, Zhuozhao van den Heuvel, Edwin R. |
author_sort | Post, Richard A. J. |
collection | PubMed |
description | BACKGROUND: To reduce the transmission of the severe acute respiratory syndrome coronavirus 2 in its first wave, European governments have implemented successive measures to encourage social distancing. However, it remained unclear how effectively measures reduced the spread of the virus. We examined how the effective-contact rate (ECR), the mean number of daily contacts for an infectious individual to transmit the virus, among European citizens evolved during this wave over the period with implemented measures, disregarding a priori information on governmental measures. METHODS: We developed a data-oriented approach that is based on an extended Susceptible-Exposed-Infectious-Removed (SEIR) model. Using the available data on the confirmed numbers of infections and hospitalizations, we first estimated the daily total number of infectious-, exposed- and susceptible individuals and subsequently estimated the ECR with an iterative Poisson regression model. We then compared change points in the daily ECRs to the moments of the governmental measures. RESULTS: The change points in the daily ECRs were found to align with the implementation of governmental interventions. At the end of the considered time-window, we found similar ECRs for Italy (0.29), Spain (0.24), and Germany (0.27), while the ECR in the Netherlands (0.34), Belgium (0.35) and the UK (0.37) were somewhat higher. The highest ECR was found for Sweden (0.45). CONCLUSIONS: There seemed to be an immediate effect of banning events and closing schools, typically among the first measures taken by the governments. The effect of additionally closing bars and restaurants seemed limited. For most countries a somewhat delayed effect of the full lockdown was observed, and the ECR after a full lockdown was not necessarily lower than an ECR after (only) a gathering ban. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10257-2. |
format | Online Article Text |
id | pubmed-7908011 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-79080112021-02-26 How did governmental interventions affect the spread of COVID-19 in European countries? Post, Richard A. J. Regis, Marta Zhan, Zhuozhao van den Heuvel, Edwin R. BMC Public Health Research Article BACKGROUND: To reduce the transmission of the severe acute respiratory syndrome coronavirus 2 in its first wave, European governments have implemented successive measures to encourage social distancing. However, it remained unclear how effectively measures reduced the spread of the virus. We examined how the effective-contact rate (ECR), the mean number of daily contacts for an infectious individual to transmit the virus, among European citizens evolved during this wave over the period with implemented measures, disregarding a priori information on governmental measures. METHODS: We developed a data-oriented approach that is based on an extended Susceptible-Exposed-Infectious-Removed (SEIR) model. Using the available data on the confirmed numbers of infections and hospitalizations, we first estimated the daily total number of infectious-, exposed- and susceptible individuals and subsequently estimated the ECR with an iterative Poisson regression model. We then compared change points in the daily ECRs to the moments of the governmental measures. RESULTS: The change points in the daily ECRs were found to align with the implementation of governmental interventions. At the end of the considered time-window, we found similar ECRs for Italy (0.29), Spain (0.24), and Germany (0.27), while the ECR in the Netherlands (0.34), Belgium (0.35) and the UK (0.37) were somewhat higher. The highest ECR was found for Sweden (0.45). CONCLUSIONS: There seemed to be an immediate effect of banning events and closing schools, typically among the first measures taken by the governments. The effect of additionally closing bars and restaurants seemed limited. For most countries a somewhat delayed effect of the full lockdown was observed, and the ECR after a full lockdown was not necessarily lower than an ECR after (only) a gathering ban. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10257-2. BioMed Central 2021-02-26 /pmc/articles/PMC7908011/ /pubmed/33637062 http://dx.doi.org/10.1186/s12889-021-10257-2 Text en © The Author(s) 2021 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Post, Richard A. J. Regis, Marta Zhan, Zhuozhao van den Heuvel, Edwin R. How did governmental interventions affect the spread of COVID-19 in European countries? |
title | How did governmental interventions affect the spread of COVID-19 in European countries? |
title_full | How did governmental interventions affect the spread of COVID-19 in European countries? |
title_fullStr | How did governmental interventions affect the spread of COVID-19 in European countries? |
title_full_unstemmed | How did governmental interventions affect the spread of COVID-19 in European countries? |
title_short | How did governmental interventions affect the spread of COVID-19 in European countries? |
title_sort | how did governmental interventions affect the spread of covid-19 in european countries? |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7908011/ https://www.ncbi.nlm.nih.gov/pubmed/33637062 http://dx.doi.org/10.1186/s12889-021-10257-2 |
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