Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/107471
DC FieldValueLanguage
dc.contributor.authorPerez, Francisco Javier Diazen_US
dc.contributor.authorChinarro, Daviden_US
dc.contributor.authorOtin, Rosa Pinoen_US
dc.contributor.authorMartín, Ricardo Díazen_US
dc.contributor.authorDíaz Cabrera, Moisésen_US
dc.contributor.authorMouhaffel, Adib Guardiolaen_US
dc.date.accessioned2021-06-09T11:45:25Z-
dc.date.available2021-06-09T11:45:25Z-
dc.date.issued2020en_US
dc.identifier.issn2076-9172en_US
dc.identifier.urihttp://hdl.handle.net/10553/107471-
dc.description.abstractOn May 19, 2020, data confirmed that coronavirus 2019 disease (COVID-19) had spread worldwide, with more than 4.7 million infected people and more than 316,000 deaths. In this article, we carry out a comparison of the methods to calculate and forecast the growth of the pandemic using two statistical models: the autoregressive integrated moving average (ARIMA) and the Gompertz function growth model. The countries that have been chosen to verify the usefulness of these models are Austria, Switzerland, and Israel, which have a similar number of habitants. The investigation to check the accuracy of the models was carried out using data on confirmed, non-asymptomatic cases and confirmed deaths from the period February 21-May 19, 2020. We use the root mean squared error (RMSE), the mean absolute percentage error (MAPE), and the regression coefficient index R2 to check the accuracy of the models. The experimental results provide promising adjustment errors for both models (R >0.99), with the ARIMA model being the best for infections and the Gompertz best for mortality. It has also been verified that countries are affected differently, which may be due to external factors that are difficult to measure quantitatively. These models provide a fast and effective system to check the growth of pandemics that can be useful for health systems and politicians so that appropriate measures are taken and countries' health care systems do not collapse. 2en_US
dc.languageengen_US
dc.relation.ispartofRambam Maimonides Medical Journalen_US
dc.sourceRambam Maimonides Medical Journal [ISSN 2076-9172], v. 11 (3), eoo22. Special Issue on the COVID-19 Pandemic with Guest Editors Oren Caspi, M.D. and Ami Neuberger, M.D.en_US
dc.subject.otherARIMAen_US
dc.subject.otherCoronavirusen_US
dc.subject.otherCOVID-19en_US
dc.subject.otherGompertzen_US
dc.subject.otherGrowth modelen_US
dc.titleComparison of growth patterns of COVID-19 cases through the arima and gompertz models. case studies: Austria, Switzerland, and Israelen_US
dc.typeArticleen_US
dc.identifier.doi10.5041/RMMJ.10413en_US
dc.identifier.scopus2-s2.0-85096328005-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.identifier.issue3-
dc.relation.volume11en_US
dc.investigacionCienciasen_US
dc.type2Artículoen_US
dc.description.numberofpages13en_US
dc.utils.revisionen_US
dc.date.coverdateJulio 2020en_US
dc.identifier.ulpgcNoen_US
dc.contributor.buulpgcBU-BASen_US
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptDepartamento de Física-
crisitem.author.orcid0000-0003-3878-3867-
crisitem.author.fullNameDíaz Cabrera, Moisés-
Appears in Collections:Artículos
Thumbnail
PDF
Adobe PDF (1,22 MB)
Show simple item record

SCOPUSTM   
Citations

4
checked on Jun 20, 2021

Page view(s)

37
checked on Jun 21, 2021

Download(s)

12
checked on Jun 21, 2021

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.