Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers

Detalhes bibliográficos
Autor(a) principal: Fernandes, Leonardo H. S.
Data de Publicação: 2021
Outros Autores: Araujo, Fernando H. A., Silva, Maria A. R., Acioli-Santos, Bartolomeu
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da FIOCRUZ (ARCA)
Texto Completo: https://www.arca.fiocruz.br/handle/icict/52141
Resumo: Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
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spelling Fernandes, Leonardo H. S.Araujo, Fernando H. A.Silva, Maria A. R.Acioli-Santos, Bartolomeu2022-04-10T22:53:15Z2022-04-10T22:53:15Z2021FERNANDES, Leonardo H. S. et al. Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers. Results in Physics, v. 26, p. 1-12, July 2021.2211-3797https://www.arca.fiocruz.br/handle/icict/5214110.1016/j.rinp.2021.104306Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.Federal Rural University of Pernambuco. Department of Economics and Informatics. Serra Talhada, PE, Brazil.Federal Rural University of Pernambuco. Department of Statistics and Informatics. Recife, PE, Brazil.Federal Institute of Education, Science and Technology of Paraíba. Department of Biology. Campus Cabedelo, PB, Brazil.Oswaldo Cruz Foundation. Aggeu Magalhães Institute. Department of Virology. Recife, PE, Brazil/This paper examines the predictability of COVID-19 worldwide lethality considering 43 countries. Based on the values inherent to Permutation entropy (Hs) and Fisher information measure (), we apply the Shannon-Fisher causality plane (SFCP), which allows us to quantify the disorder an evaluate randomness present in the time series of daily death cases related to COVID-19 in each country. We also use Hs and Fs to rank the COVID-19 lethality in these countries based on the complexity hierarchy. Our results suggest that the most proactive countries implemented measures such as facemasks, social distancing, quarantine, massive population testing, and hygienic (sanitary) orientations to limit the impacts of COVID-19, which implied lower entropy (higher predictability) to the COVID-19 lethality. In contrast, the most reactive countries implementing these measures depicted higher entropy (lower predictability) to the COVID-19 lethality. Given this, our findings shed light that these preventive measures are efficient to combat the COVID-19 lethality.engElsevierPredictability of COVID-19 worldwide lethality using permutation-information theory quantifiersinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleCOVID-19LethalityPermutation entropyFisher information measureComplexity hierarchySliding window techniqueinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-83134https://www.arca.fiocruz.br/bitstream/icict/52141/1/license.txt0ab46789d568c4ba27c7b5d29a9fe9c4MD51ORIGINALFernandes_Leonardo_etal_IAM_COVID-19_2021.pdfFernandes_Leonardo_etal_IAM_COVID-19_2021.pdfapplication/pdf3215663https://www.arca.fiocruz.br/bitstream/icict/52141/2/Fernandes_Leonardo_etal_IAM_COVID-19_2021.pdf40ab4fde2a7699068995b9ba58b37fdaMD52icict/521412022-04-10 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dc.title.pt_BR.fl_str_mv Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers
title Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers
spellingShingle Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers
Fernandes, Leonardo H. S.
COVID-19
Lethality
Permutation entropy
Fisher information measure
Complexity hierarchy
Sliding window technique
title_short Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers
title_full Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers
title_fullStr Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers
title_full_unstemmed Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers
title_sort Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers
author Fernandes, Leonardo H. S.
author_facet Fernandes, Leonardo H. S.
Araujo, Fernando H. A.
Silva, Maria A. R.
Acioli-Santos, Bartolomeu
author_role author
author2 Araujo, Fernando H. A.
Silva, Maria A. R.
Acioli-Santos, Bartolomeu
author2_role author
author
author
dc.contributor.author.fl_str_mv Fernandes, Leonardo H. S.
Araujo, Fernando H. A.
Silva, Maria A. R.
Acioli-Santos, Bartolomeu
dc.subject.en.pt_BR.fl_str_mv COVID-19
Lethality
Permutation entropy
Fisher information measure
Complexity hierarchy
Sliding window technique
topic COVID-19
Lethality
Permutation entropy
Fisher information measure
Complexity hierarchy
Sliding window technique
description Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
publishDate 2021
dc.date.issued.fl_str_mv 2021
dc.date.accessioned.fl_str_mv 2022-04-10T22:53:15Z
dc.date.available.fl_str_mv 2022-04-10T22:53:15Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.citation.fl_str_mv FERNANDES, Leonardo H. S. et al. Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers. Results in Physics, v. 26, p. 1-12, July 2021.
dc.identifier.uri.fl_str_mv https://www.arca.fiocruz.br/handle/icict/52141
dc.identifier.issn.pt_BR.fl_str_mv 2211-3797
dc.identifier.doi.none.fl_str_mv 10.1016/j.rinp.2021.104306
identifier_str_mv FERNANDES, Leonardo H. S. et al. Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers. Results in Physics, v. 26, p. 1-12, July 2021.
2211-3797
10.1016/j.rinp.2021.104306
url https://www.arca.fiocruz.br/handle/icict/52141
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dc.publisher.none.fl_str_mv Elsevier
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https://www.arca.fiocruz.br/bitstream/icict/52141/2/Fernandes_Leonardo_etal_IAM_COVID-19_2021.pdf
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