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Application of the ARIMA model on the COVID-2019 epidemic dataset

Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. H...

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Autores principales: Benvenuto, Domenico, Giovanetti, Marta, Vassallo, Lazzaro, Angeletti, Silvia, Ciccozzi, Massimo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063124/
https://www.ncbi.nlm.nih.gov/pubmed/32181302
http://dx.doi.org/10.1016/j.dib.2020.105340
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author Benvenuto, Domenico
Giovanetti, Marta
Vassallo, Lazzaro
Angeletti, Silvia
Ciccozzi, Massimo
author_facet Benvenuto, Domenico
Giovanetti, Marta
Vassallo, Lazzaro
Angeletti, Silvia
Ciccozzi, Massimo
author_sort Benvenuto, Domenico
collection PubMed
description Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.
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spelling pubmed-70631242020-03-16 Application of the ARIMA model on the COVID-2019 epidemic dataset Benvenuto, Domenico Giovanetti, Marta Vassallo, Lazzaro Angeletti, Silvia Ciccozzi, Massimo Data Brief Immunology and Microbiology Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time. Elsevier 2020-02-26 /pmc/articles/PMC7063124/ /pubmed/32181302 http://dx.doi.org/10.1016/j.dib.2020.105340 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Immunology and Microbiology
Benvenuto, Domenico
Giovanetti, Marta
Vassallo, Lazzaro
Angeletti, Silvia
Ciccozzi, Massimo
Application of the ARIMA model on the COVID-2019 epidemic dataset
title Application of the ARIMA model on the COVID-2019 epidemic dataset
title_full Application of the ARIMA model on the COVID-2019 epidemic dataset
title_fullStr Application of the ARIMA model on the COVID-2019 epidemic dataset
title_full_unstemmed Application of the ARIMA model on the COVID-2019 epidemic dataset
title_short Application of the ARIMA model on the COVID-2019 epidemic dataset
title_sort application of the arima model on the covid-2019 epidemic dataset
topic Immunology and Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063124/
https://www.ncbi.nlm.nih.gov/pubmed/32181302
http://dx.doi.org/10.1016/j.dib.2020.105340
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