Country transition index based on hierarchical clustering to predict next COVID-19 waves

Abstract COVID-19 has widely spread around the world, impacting the health systems of several countries in addition to the collateral damage that societies will face in the next years. Although the comparison between countries is essential for controlling this disease, the main challenge is the fact...

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Autores principales:	Ricardo A. Rios, Tatiane Nogueira, Danilo B. Coimbra, Tiago J. S. Lopes, Ajith Abraham, Rodrigo F. de Mello
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e7e050891cb744a7b6f28be86294a943
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spelling	oai:doaj.org-article:e7e050891cb744a7b6f28be86294a9432021-12-02T16:31:48ZCountry transition index based on hierarchical clustering to predict next COVID-19 waves10.1038/s41598-021-94661-z2045-2322https://doaj.org/article/e7e050891cb744a7b6f28be86294a9432021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94661-zhttps://doaj.org/toc/2045-2322Abstract COVID-19 has widely spread around the world, impacting the health systems of several countries in addition to the collateral damage that societies will face in the next years. Although the comparison between countries is essential for controlling this disease, the main challenge is the fact of countries are not simultaneously affected by the virus. Therefore, from the COVID-19 dataset by the Johns Hopkins University Center for Systems Science and Engineering, we present a temporal analysis on the number of new cases and deaths among countries using artificial intelligence. Our approach incrementally models the cases using a hierarchical clustering that emphasizes country transitions between infection groups over time. Then, one can compare the current situation of a country against others that have already faced previous waves. By using our approach, we designed a transition index to estimate the most probable countries’ movements between infectious groups to predict next wave trends. We draw two important conclusions: (1) we show the historical infection path taken by specific countries and emphasize changing points that occur when countries move between clusters with small, medium, or large number of cases; (2) we estimate new waves for specific countries using the transition index.Ricardo A. RiosTatiane NogueiraDanilo B. CoimbraTiago J. S. LopesAjith AbrahamRodrigo F. de MelloNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ricardo A. Rios Tatiane Nogueira Danilo B. Coimbra Tiago J. S. Lopes Ajith Abraham Rodrigo F. de Mello Country transition index based on hierarchical clustering to predict next COVID-19 waves
description	Abstract COVID-19 has widely spread around the world, impacting the health systems of several countries in addition to the collateral damage that societies will face in the next years. Although the comparison between countries is essential for controlling this disease, the main challenge is the fact of countries are not simultaneously affected by the virus. Therefore, from the COVID-19 dataset by the Johns Hopkins University Center for Systems Science and Engineering, we present a temporal analysis on the number of new cases and deaths among countries using artificial intelligence. Our approach incrementally models the cases using a hierarchical clustering that emphasizes country transitions between infection groups over time. Then, one can compare the current situation of a country against others that have already faced previous waves. By using our approach, we designed a transition index to estimate the most probable countries’ movements between infectious groups to predict next wave trends. We draw two important conclusions: (1) we show the historical infection path taken by specific countries and emphasize changing points that occur when countries move between clusters with small, medium, or large number of cases; (2) we estimate new waves for specific countries using the transition index.
format	article
author	Ricardo A. Rios Tatiane Nogueira Danilo B. Coimbra Tiago J. S. Lopes Ajith Abraham Rodrigo F. de Mello
author_facet	Ricardo A. Rios Tatiane Nogueira Danilo B. Coimbra Tiago J. S. Lopes Ajith Abraham Rodrigo F. de Mello
author_sort	Ricardo A. Rios
title	Country transition index based on hierarchical clustering to predict next COVID-19 waves
title_short	Country transition index based on hierarchical clustering to predict next COVID-19 waves
title_full	Country transition index based on hierarchical clustering to predict next COVID-19 waves
title_fullStr	Country transition index based on hierarchical clustering to predict next COVID-19 waves
title_full_unstemmed	Country transition index based on hierarchical clustering to predict next COVID-19 waves
title_sort	country transition index based on hierarchical clustering to predict next covid-19 waves
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e7e050891cb744a7b6f28be86294a943
work_keys_str_mv	AT ricardoarios countrytransitionindexbasedonhierarchicalclusteringtopredictnextcovid19waves AT tatianenogueira countrytransitionindexbasedonhierarchicalclusteringtopredictnextcovid19waves AT danilobcoimbra countrytransitionindexbasedonhierarchicalclusteringtopredictnextcovid19waves AT tiagojslopes countrytransitionindexbasedonhierarchicalclusteringtopredictnextcovid19waves AT ajithabraham countrytransitionindexbasedonhierarchicalclusteringtopredictnextcovid19waves AT rodrigofdemello countrytransitionindexbasedonhierarchicalclusteringtopredictnextcovid19waves
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Country transition index based on hierarchical clustering to predict next COVID-19 waves

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