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An evaluation of COVID-19 in Italy: A data-driven modeling analysis

The novel coronavirus (COVID-19) that has been spreading worldwide since December 2019 has sickened millions of people, lock down major cities and some countries, prompted unprecedented global travel restrictions. Real data-driven modeling is an effort to help evaluate and curb the spread of the nov...

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Autores principales: Ding, Yongmei, Gao, Liyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347309/
https://www.ncbi.nlm.nih.gov/pubmed/32766461
http://dx.doi.org/10.1016/j.idm.2020.06.007
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author Ding, Yongmei
Gao, Liyuan
author_facet Ding, Yongmei
Gao, Liyuan
author_sort Ding, Yongmei
collection PubMed
description The novel coronavirus (COVID-19) that has been spreading worldwide since December 2019 has sickened millions of people, lock down major cities and some countries, prompted unprecedented global travel restrictions. Real data-driven modeling is an effort to help evaluate and curb the spread of the novel virus. Lockdowns and the effectiveness of reduction in the contacts in Italy has been measured via our modified model, with the addition of auxiliary and state variables that represent, contacts with infected, conversion rate and latent propagation. Results show the decrease in infected people due to stay-at-home orders and tracing quarantine intervention. The effect of quarantine and centralized medical treatment was also measured through numerical modeling analysis.
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spelling pubmed-73473092020-07-10 An evaluation of COVID-19 in Italy: A data-driven modeling analysis Ding, Yongmei Gao, Liyuan Infect Dis Model Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu The novel coronavirus (COVID-19) that has been spreading worldwide since December 2019 has sickened millions of people, lock down major cities and some countries, prompted unprecedented global travel restrictions. Real data-driven modeling is an effort to help evaluate and curb the spread of the novel virus. Lockdowns and the effectiveness of reduction in the contacts in Italy has been measured via our modified model, with the addition of auxiliary and state variables that represent, contacts with infected, conversion rate and latent propagation. Results show the decrease in infected people due to stay-at-home orders and tracing quarantine intervention. The effect of quarantine and centralized medical treatment was also measured through numerical modeling analysis. KeAi Publishing 2020-07-09 /pmc/articles/PMC7347309/ /pubmed/32766461 http://dx.doi.org/10.1016/j.idm.2020.06.007 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu
Ding, Yongmei
Gao, Liyuan
An evaluation of COVID-19 in Italy: A data-driven modeling analysis
title An evaluation of COVID-19 in Italy: A data-driven modeling analysis
title_full An evaluation of COVID-19 in Italy: A data-driven modeling analysis
title_fullStr An evaluation of COVID-19 in Italy: A data-driven modeling analysis
title_full_unstemmed An evaluation of COVID-19 in Italy: A data-driven modeling analysis
title_short An evaluation of COVID-19 in Italy: A data-driven modeling analysis
title_sort evaluation of covid-19 in italy: a data-driven modeling analysis
topic Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347309/
https://www.ncbi.nlm.nih.gov/pubmed/32766461
http://dx.doi.org/10.1016/j.idm.2020.06.007
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