COVID-19: Worldwide Profiles during the First 250 Days

Detalhes bibliográficos
Autor(a) principal: António, Nuno
Data de Publicação: 2021
Outros Autores: Rita, Paulo, Saraiva, Pedro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/103700
https://doi.org/10.3390/app11083400
Resumo: 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.
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spelling COVID-19: Worldwide Profiles during the First 250 DaysCOVID-19 pandemicclusteringdata sciencemachine learningunsupervised learningThe 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.MDPI2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/103700http://hdl.handle.net/10316/103700https://doi.org/10.3390/app11083400eng2076-3417António, NunoRita, PauloSaraiva, Pedroinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-11-22T21:44:35Zoai:estudogeral.uc.pt:10316/103700Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:20:29.369169Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv COVID-19: Worldwide Profiles during the First 250 Days
title COVID-19: Worldwide Profiles during the First 250 Days
spellingShingle COVID-19: Worldwide Profiles during the First 250 Days
António, Nuno
COVID-19 pandemic
clustering
data science
machine learning
unsupervised learning
title_short COVID-19: Worldwide Profiles during the First 250 Days
title_full COVID-19: Worldwide Profiles during the First 250 Days
title_fullStr COVID-19: Worldwide Profiles during the First 250 Days
title_full_unstemmed COVID-19: Worldwide Profiles during the First 250 Days
title_sort COVID-19: Worldwide Profiles during the First 250 Days
author António, Nuno
author_facet António, Nuno
Rita, Paulo
Saraiva, Pedro
author_role author
author2 Rita, Paulo
Saraiva, Pedro
author2_role author
author
dc.contributor.author.fl_str_mv António, Nuno
Rita, Paulo
Saraiva, Pedro
dc.subject.por.fl_str_mv COVID-19 pandemic
clustering
data science
machine learning
unsupervised learning
topic COVID-19 pandemic
clustering
data science
machine learning
unsupervised learning
description 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.
publishDate 2021
dc.date.none.fl_str_mv 2021
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/103700
http://hdl.handle.net/10316/103700
https://doi.org/10.3390/app11083400
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https://doi.org/10.3390/app11083400
dc.language.iso.fl_str_mv eng
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