Data mining for the study of the Epidemic (SARS-CoV-2) COVID-19: Algorithm for the identification of patients speaking the native language in the Totonacapan area – Mexico

Carlos Medel-Ramírez and Hilario Medel-López

MPRA Paper from University Library of Munich, Germany

Abstract: The importance of the working document is that it allows analyzing the information and the status of the cases associated with (SARS-CoV-2) COVID-19 as data open to the municipal government and especially in the Totonacapan Zone in Mexico, from the registry patient diary, according to age, sex, comorbidities and condition of (SARS-CoV-2) COVID-19, according to the following characteristics: a) Positive, b) Negative, c) Suspect. Likewise, it presents information on the identification of an outpatient and / or hospitalized patient, attending to their medical development, identifying: a) Recovered, b) Deaths and c) Assets. Data analysis is carried out by applying a data mining algorithm, which provides the information, fast and timely, necessary for the estimation of the healthcare scenarios of (SARS-CoV-2) COVID-19.

Keywords: (SARS-CoV-2) COVID-19; Algorithm (SARS-CoV-2) COVID-19; Totonacapan Zone; Mexico; Identification of patients; Native language (search for similar items in EconPapers)
JEL-codes: C02 C80 C89 I19 I38 (search for similar items in EconPapers)
Date: 2020-07-24
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Citations: View citations in EconPapers (2)

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