Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach

Carrillo Larco, Rodrigo M. y Castillo Cara, Manuel . (2020) Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach. Wellcome Open Research 2020, 5:56

Ficheros (Some files may be inaccessible until you login with your e-spacio credentials)
Nombre Descripción Tipo MIME Size
Castillo_Cara_Jose_Manuel_COVID19.pdf Castillo Cara_Jose Manuel_COVID19.pdf application/pdf 2.71MB

Título Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach
Autor(es) Carrillo Larco, Rodrigo M.
Castillo Cara, Manuel
Materia(s) Informática
Abstract Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.
Palabras clave COVID-19
pandemic
clustering
k-mean
unsupervised algorithms
Editor(es) Taylor & Francis
Fecha 2020-06-15
Formato application/pdf
Identificador bibliuned:95-Jmcastillo-0006
http://e-spacio.uned.es/fez/view/bibliuned:95-Jmcastillo-0006
DOI - identifier https://doi.org/10.12688/wellcomeopenres.15819.3
ISSN - identifier 2398-502X
Nombre de la revista Wellcome Open Research
Publicado en la Revista Wellcome Open Research 2020, 5:56
Idioma eng
Versión de la publicación publishedVersion
Tipo de recurso Article
Derechos de acceso y licencia http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
Tipo de acceso Acceso abierto
Notas adicionales The registered version of this article, first published in "Wellcome Open Research 2020, 5:56", is available online at the publisher's website: https://doi.org/10.12688/wellcomeopenres.15819.3
Notas adicionales La versión registrada de este artículo, publicado por primera vez en "Wellcome Open Research 2020, 5:56", está disponible en línea en el sitio web del editor: https://doi.org/10.12688/wellcomeopenres.15819.3

 
Versiones
Versión Tipo de filtro
Contador de citas: Google Scholar Search Google Scholar
Estadísticas de acceso: 36 Visitas, 9 Descargas  -  Estadísticas en detalle
Creado: Tue, 30 Jan 2024, 00:21:31 CET