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

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

SUBMITTER: Carrillo-Larco RM 

PROVIDER: S-EPMC7308996 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature