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Title: COVID-19: Worldwide Profiles during the First 250 Days
Authors: António, Nuno
Rita, Paulo
Saraiva, Pedro 
Keywords: COVID-19 pandemic; clustering; data science; machine learning; unsupervised learning
Issue Date: 2021
Publisher: MDPI
Serial title, monograph or event: Applied Sciences (Switzerland)
Volume: 11
Issue: 8
Abstract: The present COVID-19 pandemic is happening in a strongly interconnected world. This interconnection explains why it became universal in such a short period of time and why it stimulated the creation of a large amount of relevant open data. In this paper, we use data science tools to explore this open data from the moment the pandemic began and across the first 250 days of prevalence before vaccination started. The use of unsupervised machine learning techniques allowed us to identify three clusters of countries and territories with similar profiles of standardized COVID-19 time dynamics. Although countries and territories in the three clusters share some characteristics, their composition is not homogenous. All these clusters contain countries from different geographies and with different development levels. The use of descriptive statistics and data visualization techniques enabled the description and understanding of where and how COVID-19 was impacting. Some interesting extracted features are discussed and suggestions for future research in this area are also presented.
ISSN: 2076-3417
DOI: 10.3390/app11083400
Rights: openAccess
Appears in Collections:I&D CIEPQPF - Artigos em Revistas Internacionais

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