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Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks
Against the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to mitigate it. However, it is generally difficult to estimate the joint impact of different control strategies. In this paper, we tackle this question with an extended epidemic SE...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139462/ https://www.ncbi.nlm.nih.gov/pubmed/34019589 http://dx.doi.org/10.1371/journal.pone.0252019 |
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author | Proverbio, Daniele Kemp, Françoise Magni, Stefano Husch, Andreas Aalto, Atte Mombaerts, Laurent Skupin, Alexander Gonçalves, Jorge Ameijeiras-Alonso, Jose Ley, Christophe |
author_facet | Proverbio, Daniele Kemp, Françoise Magni, Stefano Husch, Andreas Aalto, Atte Mombaerts, Laurent Skupin, Alexander Gonçalves, Jorge Ameijeiras-Alonso, Jose Ley, Christophe |
author_sort | Proverbio, Daniele |
collection | PubMed |
description | Against the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to mitigate it. However, it is generally difficult to estimate the joint impact of different control strategies. In this paper, we tackle this question with an extended epidemic SEIR model, informed by a socio-political classification of different interventions. First, we inquire the conceptual effect of mitigation parameters on the infection curve. Then, we illustrate the potential of our model to reproduce and explain empirical data from a number of countries, to perform cross-country comparisons. This gives information on the best synergies of interventions to control epidemic outbreaks while minimising impact on socio-economic needs. For instance, our results suggest that, while rapid and strong lockdown is an effective pandemic mitigation measure, a combination of social distancing and early contact tracing can achieve similar mitigation synergistically, while keeping lower isolation rates. This quantitative understanding can support the establishment of mid- and long-term interventions, to prepare containment strategies against further outbreaks. This paper also provides an online tool that allows researchers and decision makers to interactively simulate diverse scenarios with our model. |
format | Online Article Text |
id | pubmed-8139462 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81394622021-06-02 Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks Proverbio, Daniele Kemp, Françoise Magni, Stefano Husch, Andreas Aalto, Atte Mombaerts, Laurent Skupin, Alexander Gonçalves, Jorge Ameijeiras-Alonso, Jose Ley, Christophe PLoS One Research Article Against the current COVID-19 pandemic, governments worldwide have devised a variety of non-pharmaceutical interventions to mitigate it. However, it is generally difficult to estimate the joint impact of different control strategies. In this paper, we tackle this question with an extended epidemic SEIR model, informed by a socio-political classification of different interventions. First, we inquire the conceptual effect of mitigation parameters on the infection curve. Then, we illustrate the potential of our model to reproduce and explain empirical data from a number of countries, to perform cross-country comparisons. This gives information on the best synergies of interventions to control epidemic outbreaks while minimising impact on socio-economic needs. For instance, our results suggest that, while rapid and strong lockdown is an effective pandemic mitigation measure, a combination of social distancing and early contact tracing can achieve similar mitigation synergistically, while keeping lower isolation rates. This quantitative understanding can support the establishment of mid- and long-term interventions, to prepare containment strategies against further outbreaks. This paper also provides an online tool that allows researchers and decision makers to interactively simulate diverse scenarios with our model. Public Library of Science 2021-05-21 /pmc/articles/PMC8139462/ /pubmed/34019589 http://dx.doi.org/10.1371/journal.pone.0252019 Text en © 2021 Proverbio et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Proverbio, Daniele Kemp, Françoise Magni, Stefano Husch, Andreas Aalto, Atte Mombaerts, Laurent Skupin, Alexander Gonçalves, Jorge Ameijeiras-Alonso, Jose Ley, Christophe Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks |
title | Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks |
title_full | Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks |
title_fullStr | Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks |
title_full_unstemmed | Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks |
title_short | Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks |
title_sort | dynamical spqeir model assesses the effectiveness of non-pharmaceutical interventions against covid-19 epidemic outbreaks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139462/ https://www.ncbi.nlm.nih.gov/pubmed/34019589 http://dx.doi.org/10.1371/journal.pone.0252019 |
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