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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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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. |
format | Online Article Text |
id | pubmed-7063124 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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