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dc.contributor.authorCatalà Sabaté, Martí
dc.contributor.authorAlonso Muñoz, Sergio
dc.contributor.authorÁlvarez Lacalle, Enrique
dc.contributor.authorLópez Codina, Daniel
dc.contributor.authorCardona Iglesias, Pere Joan
dc.contributor.authorPrats Soler, Clara
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Física Computacional i Aplicada
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Física
dc.date.accessioned2021-03-12T15:41:08Z
dc.date.available2021-03-12T15:41:08Z
dc.date.issued2020-12-09
dc.identifier.citationCatalà, M. [et al.]. Empirical model for short-time prediction of COVID-19 spreading. "PLoS Computational Biology", 9 Desembre 2020, vol. 16, núm. 12, p. e1008431-1-e1008431-18.
dc.identifier.issn1553-7358
dc.identifier.urihttp://hdl.handle.net/2117/341579
dc.description.abstractThe appearance and fast spreading of Covid-19 took the international community by surprise. Collaboration between researchers, public health workers, and politicians has been established to deal with the epidemic. One important contribution from researchers in epidemiology is the analysis of trends so that both the current state and short-term future trends can be carefully evaluated. Gompertz model has been shown to correctly describe the dynamics of cumulative confirmed cases, since it is characterized by a decrease in growth rate showing the effect of control measures. Thus, it provides a way to systematically quantify the Covid-19 spreading velocity and it allows short-term predictions and longer-term estimations. This model has been employed to fit the cumulative cases of Covid-19 from several European countries. Results show that there are systematic differences in spreading velocity among countries. The model predictions provide a reliable picture of the short-term evolution in countries that are in the initial stages of the Covid-19 outbreak, and may permit researchers to uncover some characteristics of the long-term evolution. These predictions can also be generalized to calculate short-term hospital and intensive care units (ICU) requirements.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Sèries temporals
dc.subject.lcshCOVID-19 (Disease)
dc.subject.lcshTime-series analysis
dc.subject.lcshCorrelation (Statistics)
dc.titleEmpirical model for short-time prediction of COVID-19 spreading
dc.typeArticle
dc.subject.lemacCOVID-19 (Malaltia)
dc.subject.lemacSèries temporals -- Anàlisi
dc.subject.lemacCorrelació (Estadística)
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOCOM-SC - Grup de Biologia Computacional i Sistemes Complexos
dc.identifier.doi10.1371/journal.pcbi.1008431
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008431
dc.rights.accessOpen Access
local.identifier.drac30751961
dc.description.versionPostprint (published version)
local.citation.authorCatalà, M.; Alonso, S.; Alvarez-Lacalle, E.; Lopez, D.; Cardona, P.J.; Prats, C.
local.citation.publicationNamePLoS Computational Biology
local.citation.volume16
local.citation.number12
local.citation.startingPagee1008431-1
local.citation.endingPagee1008431-18


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