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dc.creatorReddy Chimmula, Vinay Kumar
dc.creatorZhang, Lei
dc.date.accessioned2020-07-24T19:24:34Z
dc.date.available2020-07-24T19:24:34Z
dc.date.created2020
dc.identifier.issn0960-0779spa
dc.identifier.otherhttps://doi.org/10.1016/j.chaos.2020.109864spa
dc.identifier.urihttp://hdl.handle.net/20.500.12010/11115
dc.description.abstractOn March 11th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseasesspa
dc.format.extent6 páginasspa
dc.format.mimetypeimage/jepgspa
dc.publisherChaos, Solitons & Fractalseng
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectEpidemic transmissionspa
dc.subjectTime series forecastingspa
dc.subjectMachine learningspa
dc.subjectCorona virusspa
dc.subjectCOVID-19spa
dc.subjectLong short term memory (LSTM) networksspa
dc.titleTime series forecasting of COVID-19 transmission in Canada using LSTM networksspa
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.2020.109864spa


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