COVID-19: Worldwide Profiles during the First 250 Days
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 ope...
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doaj-8f4958ff8cc6491986cb5fe3fcdc79412021-04-10T23:01:17ZengMDPI AGApplied Sciences2076-34172021-04-01113400340010.3390/app11083400COVID-19: Worldwide Profiles during the First 250 DaysNuno António0Paulo Rita1Pedro Saraiva2NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, 1070-312 Lisbon, PortugalNOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, 1070-312 Lisbon, PortugalNOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, 1070-312 Lisbon, PortugalThe 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.https://www.mdpi.com/2076-3417/11/8/3400COVID-19 pandemicclusteringdata sciencemachine learningunsupervised learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nuno António Paulo Rita Pedro Saraiva |
spellingShingle |
Nuno António Paulo Rita Pedro Saraiva COVID-19: Worldwide Profiles during the First 250 Days Applied Sciences COVID-19 pandemic clustering data science machine learning unsupervised learning |
author_facet |
Nuno António Paulo Rita Pedro Saraiva |
author_sort |
Nuno António |
title |
COVID-19: Worldwide Profiles during the First 250 Days |
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 |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-04-01 |
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. |
topic |
COVID-19 pandemic clustering data science machine learning unsupervised learning |
url |
https://www.mdpi.com/2076-3417/11/8/3400 |
work_keys_str_mv |
AT nunoantonio covid19worldwideprofilesduringthefirst250days AT paulorita covid19worldwideprofilesduringthefirst250days AT pedrosaraiva covid19worldwideprofilesduringthefirst250days |
_version_ |
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