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

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.

Abstract

The importance of the working document is that it allows analyzing the information and status of the cases associated with (SARS-CoV-2) COVID-19 as open data at the municipal, state and national levels, with a daily registry of patients, according to age, sex, comorbidities, for the condition of (SARS-CoV-2) COVID-19 according to the following characteristics: a) Positive, b) Negatives, c) Suspects. Likewise, it presents information regarding the identification of an outpatient and / or hospitalized patient, attending to their medical development, identifying: a) Recovered, b) Deaths and c) Assets, in Phase 3 and Phase 4, at the national state and municipal level in Mexico, the data analysis is carried out by applying an algorithm of data mining, which provides the information, fast and timely, required for the estimation of Scenarios for Medical Care of the (SARS-CoV-2) COVID-19. • The Algorithm for the identification of patients (SARS-CoV-2) COVID 19 in Mexico allows to analyze at the municipal, state and national level, the registry of patients, according to age, sex, co-morbidities, for condition of (SARS-CoV-2) COVID-19 according to the following characteristics: a) Positive, b) Negative, c) Suspicious, as well as presenting information on the identification of an outpatient and / or hospitalized patient, attending to their medical development, identifying: a) Recovered, b ) Deaths and c) Assets, in Phase 3 and Phase 4, in a fast and timely manner, to support public decision-making in health matters.

• Item Type: MPRA Paper
• Language: English
• Keywords: (SARS-CoV-2) COVID-19, Algorithm (SARS-CoV-2) COVID-19, Mexico, identification of patients
• Subjects: A - General Economics and Teaching > A2 - Economic Education and Teaching of Economics > A23 - Graduate; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C83 - Survey Methods ; Sampling Methods; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C88 - Other Computer Software; H - Public Economics > H1 - Structure and Scope of Government > H12 - Crisis Management; I - Health, Education, and Welfare > I1 - Health > I18 - Government Policy ; Regulation ; Public Health

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

[4] 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: [5] Raw data can be retrieved from the Github repository https://github.com/CMedelR/dataCovid19/