Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/75811
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dc.contributor.authorBenítez, Domingoen_US
dc.contributor.authorMontero, Gustavoen_US
dc.contributor.authorRodríguez, Eduardoen_US
dc.contributor.authorGreiner, Daviden_US
dc.contributor.authorOliver, Alberten_US
dc.contributor.authorGonzález, Luisen_US
dc.contributor.authorMontenegro, Rafaelen_US
dc.date.accessioned2020-11-23T09:05:52Z-
dc.date.available2020-11-23T09:05:52Z-
dc.date.issued2020en_US
dc.identifier.issn2227-7390en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/75811-
dc.description.abstractA novel phenomenological epidemic model is proposed to characterize the state of infectious diseases and predict their behaviors. This model is given by a new stochastic partial differential equation that is derived from foundations of statistical physics. The analytical solution of this equation describes the spatio-temporal evolution of a Gaussian probability density function. Our proposal can be applied to several epidemic variables such as infected, deaths, or admitted-to-the-Intensive Care Unit (ICU). To measure model performance, we quantify the error of the model fit to real time-series datasets and generate forecasts for all the phases of the COVID-19, Ebola, and Zika epidemics. All parameters and model uncertainties are numerically quantified. The new model is compared with other phenomenological models such as Logistic Grow, Original, and Generalized Richards Growth models. When the models are used to describe epidemic trajectories that register infected individuals, this comparison shows that the median RMSE error and standard deviation of the residuals of the new model fit to the data are lower than the best of these growing models by, on average, 19.6% and 35.7%, respectively. Using three forecasting experiments for the COVID-19 outbreak, the median RMSE error and standard deviation of residuals are improved by the performance of our model, on average by 31.0% and 27.9%, respectively, concerning the best performance of the growth models.en_US
dc.languageengen_US
dc.relationCOVIDen_US
dc.relation.ispartofMathematicsen_US
dc.sourceMathematics [EISSN 2227-7390], v. 8 (11), p. 1-22, (Noviembre 2020)en_US
dc.subject120903 Análisis de datosen_US
dc.subject120914 Técnicas de predicción estadísticaen_US
dc.subject220510 Mecánica estadísticaen_US
dc.subject.otherForecastsen_US
dc.subject.otherModel fitting performanceen_US
dc.subject.otherParameter estimationen_US
dc.subject.otherPhenomenological epidemic modelsen_US
dc.subject.otherStochastic epidemic modelsen_US
dc.titleA phenomenological epidemic model based on the spatio-temporal evolution of a gaussian probability density functionen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/math8112000en_US
dc.identifier.scopus85096011096-
dc.contributor.authorscopusid7003286582-
dc.contributor.authorscopusid56256002000-
dc.contributor.authorscopusid7401953314-
dc.contributor.authorscopusid56268125800-
dc.contributor.authorscopusid57215071329-
dc.contributor.authorscopusid35248076500-
dc.contributor.authorscopusid35617533100-
dc.identifier.eissn2227-7390-
dc.description.lastpage22en_US
dc.identifier.issue11-
dc.description.firstpage1en_US
dc.relation.volume8en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.date.coverdateNoviembre 2020en_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INFen_US
dc.description.sjr0,495
dc.description.jcr2,258
dc.description.sjrqQ2
dc.description.jcrqQ1
dc.description.scieSCIE
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR SIANI: Modelización y Simulación Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptDepartamento de Matemáticas-
crisitem.author.deptGIR SIANI: Modelización y Simulación Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptGIR SIANI: Computación Evolutiva y Aplicaciones-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Ingeniería Civil-
crisitem.author.deptGIR SIANI: Modelización y Simulación Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Matemáticas-
crisitem.author.deptDepartamento de Matemáticas-
crisitem.author.deptDepartamento de Matemáticas-
crisitem.author.orcid0000-0003-2952-2972-
crisitem.author.orcid0000-0001-5641-442X-
crisitem.author.orcid0000-0002-2701-2971-
crisitem.author.orcid0000-0002-4132-7144-
crisitem.author.orcid0000-0002-3783-8670-
crisitem.author.orcid0000-0002-4164-457X-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameBenítez Díaz, Domingo Juan-
crisitem.author.fullNameMontero García, Gustavo-
crisitem.author.fullNameRodríguez Barrera, Eduardo Miguel-
crisitem.author.fullNameGreiner Sánchez, David Juan-
crisitem.author.fullNameOliver Serra, Albert-
crisitem.author.fullNameGonzález Sánchez, Luis-
crisitem.author.fullNameMontenegro Armas, Rafael-
Colección:Artículos
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