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A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic
The COVID-19 epidemic hit Italy particularly hard, yielding the implementation of strict national lockdown rules. Previous modelling studies at the national level overlooked the fact that Italy is divided into administrative regions which can independently oversee their own share of the Italian Nati...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7547104/ https://www.ncbi.nlm.nih.gov/pubmed/33037190 http://dx.doi.org/10.1038/s41467-020-18827-5 |
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author | Della Rossa, Fabio Salzano, Davide Di Meglio, Anna De Lellis, Francesco Coraggio, Marco Calabrese, Carmela Guarino, Agostino Cardona-Rivera, Ricardo De Lellis, Pietro Liuzza, Davide Lo Iudice, Francesco Russo, Giovanni di Bernardo, Mario |
author_facet | Della Rossa, Fabio Salzano, Davide Di Meglio, Anna De Lellis, Francesco Coraggio, Marco Calabrese, Carmela Guarino, Agostino Cardona-Rivera, Ricardo De Lellis, Pietro Liuzza, Davide Lo Iudice, Francesco Russo, Giovanni di Bernardo, Mario |
author_sort | Della Rossa, Fabio |
collection | PubMed |
description | The COVID-19 epidemic hit Italy particularly hard, yielding the implementation of strict national lockdown rules. Previous modelling studies at the national level overlooked the fact that Italy is divided into administrative regions which can independently oversee their own share of the Italian National Health Service. Here, we show that heterogeneity between regions is essential to understand the spread of the epidemic and to design effective strategies to control the disease. We model Italy as a network of regions and parameterize the model of each region on real data spanning over two months from the initial outbreak. We confirm the effectiveness at the regional level of the national lockdown strategy and propose coordinated regional interventions to prevent future national lockdowns, while avoiding saturation of the regional health systems and mitigating impact on costs. Our study and methodology can be easily extended to other levels of granularity to support policy- and decision-makers. |
format | Online Article Text |
id | pubmed-7547104 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75471042020-10-19 A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic Della Rossa, Fabio Salzano, Davide Di Meglio, Anna De Lellis, Francesco Coraggio, Marco Calabrese, Carmela Guarino, Agostino Cardona-Rivera, Ricardo De Lellis, Pietro Liuzza, Davide Lo Iudice, Francesco Russo, Giovanni di Bernardo, Mario Nat Commun Article The COVID-19 epidemic hit Italy particularly hard, yielding the implementation of strict national lockdown rules. Previous modelling studies at the national level overlooked the fact that Italy is divided into administrative regions which can independently oversee their own share of the Italian National Health Service. Here, we show that heterogeneity between regions is essential to understand the spread of the epidemic and to design effective strategies to control the disease. We model Italy as a network of regions and parameterize the model of each region on real data spanning over two months from the initial outbreak. We confirm the effectiveness at the regional level of the national lockdown strategy and propose coordinated regional interventions to prevent future national lockdowns, while avoiding saturation of the regional health systems and mitigating impact on costs. Our study and methodology can be easily extended to other levels of granularity to support policy- and decision-makers. Nature Publishing Group UK 2020-10-09 /pmc/articles/PMC7547104/ /pubmed/33037190 http://dx.doi.org/10.1038/s41467-020-18827-5 Text en © The Author(s) 2020 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/. |
spellingShingle | Article Della Rossa, Fabio Salzano, Davide Di Meglio, Anna De Lellis, Francesco Coraggio, Marco Calabrese, Carmela Guarino, Agostino Cardona-Rivera, Ricardo De Lellis, Pietro Liuzza, Davide Lo Iudice, Francesco Russo, Giovanni di Bernardo, Mario A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic |
title | A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic |
title_full | A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic |
title_fullStr | A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic |
title_full_unstemmed | A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic |
title_short | A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic |
title_sort | network model of italy shows that intermittent regional strategies can alleviate the covid-19 epidemic |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7547104/ https://www.ncbi.nlm.nih.gov/pubmed/33037190 http://dx.doi.org/10.1038/s41467-020-18827-5 |
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