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

Medel-Ramírez, Carlos and Medel-López, Hilario (2020): 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.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: (SARS-CoV-2) COVID-19, Algorithm (SARS-CoV-2) COVID-19, Totonacapan Zone, Mexico, Identification of patients, Native language
• Subjects: C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C80 - General; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C89 - Other; I - Health, Education, and Welfare > I1 - Health > I19 - Other; I - Health, Education, and Welfare > I3 - Welfare, Well-Being, and Poverty > I38 - Government Policy ; Provision and Effects of Welfare Programs

References

[1] Demsar J, Curk T, Erjavec A, Gorup C, Hocevar T, Milutinovic M, Mozina M, Polajnar M, Toplak M, Staric A, Stajdohar M, Umek L, Zagar L, Zbontar J, Zitnik M, Zupan B (2013) Orange: Data Mining Toolbox in Python, Journal of Machine Learning Research 14(Aug): 2349−2353. https://dl.acm.org/doi/pdf/10.5555/2567709.2567736

[2] Government of Mexico. Health Secretary. Databases Covid-19 México. https://datos.gob.mx/busca/dataset/informacion-referente-a-casos-covid-19-en- mexico/resource/e8c7079c-dc2a-4b6e-8035-08042ed37165

[3] Medel-Ramírez, Carlos and Medel-López, Hilario, (2020), Data Article. Data mining for the study of the Epidemic (SARS- CoV-2) COVID-19: Algorithm for the identification of patients (SARS-CoV-2) COVID 19 in Mexico, MPRA Paper, University Library of Munich, Germany, https://EconPapers.repec.org/RePEc:pra:mprapa:100888

[4] Software Orange Data Mining version 3.25.1 https://orange.biolab.si

[5] World Health Organization. (2020). Laboratory testing for coronavirus disease (COVID-19) in suspected human cases. Interim guidance. 19 March 2020. Recuperado de: https://www.who.int/publications- detail/laboratory-testing-for-2019-novel-coronavirus-in-suspected-human-cases-20200117

Reference to a dataset: [6] Raw data can be retrieved from the Github repository https://github.com/CMedelR/COVID-19-Algorithm-for-the-identification-of-patients-speaking-the-native-language-in-the-Totonacap