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COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach

BACKGROUND: Till 31 March 2020, 105,792 COVID-19 cases were confirmed in Italy including 15,726 deaths which explains how worst the epidemic has affected the country. After the announcement of lockdown in Italy on 9 March 2020, situation was becoming stable since last days of March. In view of this,...

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Autores principales: Chintalapudi, Nalini, Battineni, Gopi, Amenta, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taiwan Society of Microbiology. Published by Elsevier Taiwan LLC. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7152918/
https://www.ncbi.nlm.nih.gov/pubmed/32305271
http://dx.doi.org/10.1016/j.jmii.2020.04.004
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author Chintalapudi, Nalini
Battineni, Gopi
Amenta, Francesco
author_facet Chintalapudi, Nalini
Battineni, Gopi
Amenta, Francesco
author_sort Chintalapudi, Nalini
collection PubMed
description BACKGROUND: Till 31 March 2020, 105,792 COVID-19 cases were confirmed in Italy including 15,726 deaths which explains how worst the epidemic has affected the country. After the announcement of lockdown in Italy on 9 March 2020, situation was becoming stable since last days of March. In view of this, it is important to forecast the COVID-19 evaluation of Italy condition and the possible effects, if this lock down could continue for another 60 days. METHODS: COVID-19 infected patient data has extracted from the Italian Health Ministry website includes registered and recovered cases from mid February to end March. Adoption of seasonal ARIMA forecasting package with R statistical model was done. RESULTS: Predictions were done with 93.75% of accuracy for registered case models and 84.4% of accuracy for recovered case models. The forecasting of infected patients could be reach the value of 182,757, and recovered cases could be registered value of 81,635 at end of May. CONCLUSIONS: This study highlights the importance of country lockdown and self isolation in control the disease transmissibility among Italian population through data driven model analysis. Our findings suggest that nearly 35% decrement of registered cases and 66% growth of recovered cases will be possible.
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spelling pubmed-71529182020-04-13 COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach Chintalapudi, Nalini Battineni, Gopi Amenta, Francesco J Microbiol Immunol Infect Original Article BACKGROUND: Till 31 March 2020, 105,792 COVID-19 cases were confirmed in Italy including 15,726 deaths which explains how worst the epidemic has affected the country. After the announcement of lockdown in Italy on 9 March 2020, situation was becoming stable since last days of March. In view of this, it is important to forecast the COVID-19 evaluation of Italy condition and the possible effects, if this lock down could continue for another 60 days. METHODS: COVID-19 infected patient data has extracted from the Italian Health Ministry website includes registered and recovered cases from mid February to end March. Adoption of seasonal ARIMA forecasting package with R statistical model was done. RESULTS: Predictions were done with 93.75% of accuracy for registered case models and 84.4% of accuracy for recovered case models. The forecasting of infected patients could be reach the value of 182,757, and recovered cases could be registered value of 81,635 at end of May. CONCLUSIONS: This study highlights the importance of country lockdown and self isolation in control the disease transmissibility among Italian population through data driven model analysis. Our findings suggest that nearly 35% decrement of registered cases and 66% growth of recovered cases will be possible. Taiwan Society of Microbiology. Published by Elsevier Taiwan LLC. 2020-06 2020-04-13 /pmc/articles/PMC7152918/ /pubmed/32305271 http://dx.doi.org/10.1016/j.jmii.2020.04.004 Text en © 2020 Taiwan Society of Microbiology. Published by Elsevier Taiwan LLC. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Original Article
Chintalapudi, Nalini
Battineni, Gopi
Amenta, Francesco
COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach
title COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach
title_full COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach
title_fullStr COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach
title_full_unstemmed COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach
title_short COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach
title_sort covid-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in italy: a data driven model approach
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7152918/
https://www.ncbi.nlm.nih.gov/pubmed/32305271
http://dx.doi.org/10.1016/j.jmii.2020.04.004
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