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Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region
BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), the causative agent of the coronavirus disease 19 (COVID-19), is a highly transmittable virus. Since the first person-to-person transmission of SARS-CoV-2 was reported in Italy on February 21(st), 2020, the number of people in...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592194/ https://www.ncbi.nlm.nih.gov/pubmed/33115434 http://dx.doi.org/10.1186/s12879-020-05490-w |
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author | Pernice, Simone Castagno, Paolo Marcotulli, Linda Maule, Milena Maria Richiardi, Lorenzo Moirano, Giovenale Sereno, Matteo Cordero, Francesca Beccuti, Marco |
author_facet | Pernice, Simone Castagno, Paolo Marcotulli, Linda Maule, Milena Maria Richiardi, Lorenzo Moirano, Giovenale Sereno, Matteo Cordero, Francesca Beccuti, Marco |
author_sort | Pernice, Simone |
collection | PubMed |
description | BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), the causative agent of the coronavirus disease 19 (COVID-19), is a highly transmittable virus. Since the first person-to-person transmission of SARS-CoV-2 was reported in Italy on February 21(st), 2020, the number of people infected with SARS-COV-2 increased rapidly, mainly in northern Italian regions, including Piedmont. A strict lockdown was imposed on March 21(st) until May 4(th) when a gradual relaxation of the restrictions started. In this context, computational models and computer simulations are one of the available research tools that epidemiologists can exploit to understand the spread of the diseases and to evaluate social measures to counteract, mitigate or delay the spread of the epidemic. METHODS: This study presents an extended version of the Susceptible-Exposed-Infected-Removed-Susceptible (SEIRS) model accounting for population age structure. The infectious population is divided into three sub-groups: (i) undetected infected individuals, (ii) quarantined infected individuals and (iii) hospitalized infected individuals. Moreover, the strength of the government restriction measures and the related population response to these are explicitly represented in the model. RESULTS: The proposed model allows us to investigate different scenarios of the COVID-19 spread in Piedmont and the implementation of different infection-control measures and testing approaches. The results show that the implemented control measures have proven effective in containing the epidemic, mitigating the potential dangerous impact of a large proportion of undetected cases. We also forecast the optimal combination of individual-level measures and community surveillance to contain the new wave of COVID-19 spread after the re-opening work and social activities. CONCLUSIONS: Our model is an effective tool useful to investigate different scenarios and to inform policy makers about the potential impact of different control strategies. This will be crucial in the upcoming months, when very critical decisions about easing control measures will need to be taken. |
format | Online Article Text |
id | pubmed-7592194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75921942020-10-28 Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region Pernice, Simone Castagno, Paolo Marcotulli, Linda Maule, Milena Maria Richiardi, Lorenzo Moirano, Giovenale Sereno, Matteo Cordero, Francesca Beccuti, Marco BMC Infect Dis Research Article BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), the causative agent of the coronavirus disease 19 (COVID-19), is a highly transmittable virus. Since the first person-to-person transmission of SARS-CoV-2 was reported in Italy on February 21(st), 2020, the number of people infected with SARS-COV-2 increased rapidly, mainly in northern Italian regions, including Piedmont. A strict lockdown was imposed on March 21(st) until May 4(th) when a gradual relaxation of the restrictions started. In this context, computational models and computer simulations are one of the available research tools that epidemiologists can exploit to understand the spread of the diseases and to evaluate social measures to counteract, mitigate or delay the spread of the epidemic. METHODS: This study presents an extended version of the Susceptible-Exposed-Infected-Removed-Susceptible (SEIRS) model accounting for population age structure. The infectious population is divided into three sub-groups: (i) undetected infected individuals, (ii) quarantined infected individuals and (iii) hospitalized infected individuals. Moreover, the strength of the government restriction measures and the related population response to these are explicitly represented in the model. RESULTS: The proposed model allows us to investigate different scenarios of the COVID-19 spread in Piedmont and the implementation of different infection-control measures and testing approaches. The results show that the implemented control measures have proven effective in containing the epidemic, mitigating the potential dangerous impact of a large proportion of undetected cases. We also forecast the optimal combination of individual-level measures and community surveillance to contain the new wave of COVID-19 spread after the re-opening work and social activities. CONCLUSIONS: Our model is an effective tool useful to investigate different scenarios and to inform policy makers about the potential impact of different control strategies. This will be crucial in the upcoming months, when very critical decisions about easing control measures will need to be taken. BioMed Central 2020-10-28 /pmc/articles/PMC7592194/ /pubmed/33115434 http://dx.doi.org/10.1186/s12879-020-05490-w 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 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 Pernice, Simone Castagno, Paolo Marcotulli, Linda Maule, Milena Maria Richiardi, Lorenzo Moirano, Giovenale Sereno, Matteo Cordero, Francesca Beccuti, Marco Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region |
title | Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region |
title_full | Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region |
title_fullStr | Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region |
title_full_unstemmed | Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region |
title_short | Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region |
title_sort | impacts of reopening strategies for covid-19 epidemic: a modeling study in piedmont region |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592194/ https://www.ncbi.nlm.nih.gov/pubmed/33115434 http://dx.doi.org/10.1186/s12879-020-05490-w |
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