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Machine Learning in the analysis of lethality and evolution of infection by the SARS-CoV-2 virus (COVID-19) in workers of the Mexico City Metro
Erendira Itzel Garcia Islas; Guillermo De Anda Jauregui; Joaquin Salas Rodriguez; Florencia Serrania Soto.
Afiliação
  • Erendira Itzel Garcia Islas; Center for Complexity Sciences, National Autonomous University of Mexico (C3,UNAM)
  • Guillermo De Anda Jauregui; National Institute of Genomic Sciences (INMEGEN)
  • Joaquin Salas Rodriguez; National Polytechnic Institute (IPN)
  • Florencia Serrania Soto; Mexico City Metro (STC-CDMX)
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21265573
ABSTRACT
In terms of the number of fatalities, Mexico has been one of the countries most affected worldwide by the pandemic. Using different Machine Learning techniques, some of the first cases of the infection registered in Mexico City (CDMX), the geographical and political center of the country, are analyzed in order to determine the causes of lethality and evolution of infection by the SARS-CoV-2 virus, from April 1 to September 27, 2020 in workers of the Capital Metro.
Licença
cc_by_nc
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
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