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dc.creatorArias Velásquez, Ricardo Manuel
dc.creatorMejía Lara, Jennifer Vanessa
dc.date.accessioned2020-07-28T19:24:57Z
dc.date.available2020-07-28T19:24:57Z
dc.date.created2020
dc.identifier.issn0960-0779spa
dc.identifier.otherhttps://doi.org/10.1016/j.chaos.2020.109924spa
dc.identifier.urihttp://hdl.handle.net/20.500.12010/11292
dc.description.abstractIn this report, we analyze historical and forecast infections for COVID-19 death based on Reduced-Space Gaussian Process Regression associated to chaotic Dynamical Systems with information obtained in 82 days with continuous learning, day by day, from January 21th, 2020 to April 12th. According last results, COVID-19 could be predicted with Gaussian models mean-field models can be meaning- fully used to gather a quantitative picture of the epidemic spreading, with infections, fatality and recovery rate. The forecast places the peak in USA around July 14th 2020, with a peak number of 132,074 death with infected individuals of about 1,157,796 and a number of deaths at the end of the epidemics of about 132,800. Late on January, USA confirmed the first patient with COVID-19, who had recently traveled to China, however, an evaluation of states in USA have demonstrated a fatality rate in China (4%) is lower than New York (4.56%), but lower than Michigan (5.69%). Mean estimates and uncertainty bounds for both USA and his cities and other provinces have increased in the last three months, with focus on New York, New Jersey, Michigan, California, Massachusetts, ... (January e April 12th). Besides, we propose a ReducedSpace Gaussian Process Regression model predicts that the epidemic will reach saturation in USA on July 2020. Our findings suggest, new quarantine actions with more restrictions for containment strategies implemented in USA could be successfully, but in a late period, it could generate critical rate infections and death for the next 2 month.spa
dc.format.extent9 páginasspa
dc.format.mimetypeimage/jepgspa
dc.publisherChaos, Solitons and Fractalseng
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectCOVID-19spa
dc.subjectForecastspa
dc.subjectGaussianspa
dc.subjectUSAspa
dc.titleForecast and evaluation of COVID-19 spreading in USA with reduced-space Gaussian process regressionspa
dc.type.localArtículospa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.subject.lembCOVID-19spa
dc.subject.lembSARS-CoV-2spa
dc.subject.lembCoronavirusspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.identifier.doihttps://doi.org/10.1016/j.chaos.2020.109924spa


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