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

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dc.contributor.authorCarrillo Larco, Rodrigo M.
dc.contributor.authorCastillo Cara, José Manuel
dc.date.accessioned2024-05-20T11:42:08Z
dc.date.available2024-05-20T11:42:08Z
dc.date.issued2020-06-15
dc.description.abstractBackground: 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.en
dc.description.versionversión publicada
dc.identifier.doihttps://doi.org/10.12688/wellcomeopenres.15819.3
dc.identifier.issn2398-502X
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12439
dc.journal.titleWellcome Open Research
dc.language.isoen
dc.publisherTaylor & Francis
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subject.keywordsCOVID-19
dc.subject.keywordspandemic
dc.subject.keywordsclustering
dc.subject.keywordsk-mean
dc.subject.keywordsunsupervised algorithms
dc.titleUsing country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approaches
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
relation.isAuthorOfPublicationc0e39bd2-c0d8-4743-953d-488baf6b977e
relation.isAuthorOfPublication.latestForDiscoveryc0e39bd2-c0d8-4743-953d-488baf6b977e
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