dc.creator | Hossain Monda, M. Rubaiyat | |
dc.creator | Bharati, Subrato | |
dc.creator | Podder, Prajoy | |
dc.creator | Podder, Priya | |
dc.date.accessioned | 2020-07-23T14:20:43Z | |
dc.date.available | 2020-07-23T14:20:43Z | |
dc.date.created | 2020 | |
dc.identifier.issn | 2352-9148 | spa |
dc.identifier.other | https://doi.org/10.1016/j.imu.2020.100374 | spa |
dc.identifier.uri | http://hdl.handle.net/20.500.12010/11011 | |
dc.description.abstract | This paper describes different aspects of novel coronavirus disease (COVID-19), presents visualization of the
spread of the infection, and discusses the potential applications of data analytics on this viral infection. Firstly, a
literature survey is done on COVID-19 highlighting a number of factors including its origin, its similarity with
previous coronaviruses, its transmission capacity, its symptoms, etc. Secondly, data analytics is applied on a
dataset of Johns Hopkins University to find out the spread of the viral infection. It is shown here that although
the disease started in China in December 2019, the highest number of confirmed cases up to June 04, 2020 is in
the USA. Thirdly, the worldwide increase in the number of confirmed cases over time is modelled here using a
polynomial regression algorithm with degree 2. Fourthly, classification algorithms are applied on a dataset of
5644 samples provided by Hospital Israelita Albert Einstein of Brazil in order to diagnose COVID-19. It is shown
here that multilayer perceptron (MLP), XGBoost and logistic regression can classify COVID-19 patients at an
accuracy above 91%. Finally, a discussion is presented on the potential applications of data analytics in several
important factors of COVID-19. | spa |
dc.format.extent | 13 páginas | spa |
dc.format.mimetype | application/pdf | spa |
dc.publisher | Science Direct | eng |
dc.source | reponame:Expeditio Repositorio Institucional UJTL | spa |
dc.source | instname:Universidad de Bogotá Jorge Tadeo Lozano | spa |
dc.subject | Coronavirus | spa |
dc.subject | COVID-19 | spa |
dc.subject | Classification | spa |
dc.subject | Machine learning | spa |
dc.subject | Regression | spa |
dc.subject | SARS-CoV-2 | spa |
dc.title | Data analytics for novel coronavirus disease | spa |
dc.type.local | Artículo | spa |
dc.subject.lemb | Síndrome respiratorio agudo grave | spa |
dc.subject.lemb | COVID-19 | spa |
dc.subject.lemb | SARS-CoV-2 | spa |
dc.subject.lemb | Coronavirus | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.type.hasversion | info:eu-repo/semantics/acceptedVersion | spa |
dc.identifier.doi | https://doi.org/10.1016/j.imu.2020.100374 | spa |