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

Fernandes, Leonardo H S; Araujo, Fernando H A; Silva, Maria A R; Acioli-Santos, Bartolomeu

Fernandes, Leonardo H S; Araujo, Fernando H A; Silva, Maria A R; Acioli-Santos, Bartolomeu.

Afiliação

Fernandes LHS; Department of Economics and Informatics, Federal Rural University of Pernambuco, Serra Talhada, PE 56909-535, Brazil.
Araujo FHA; Department of Statistics and Informatics, Federal Rural University of Pernambuco, Recife, PE 52171-900, Brazil.
Silva MAR; Department of Biology, Federal Institute of Education, Science and Technology of Paraíba, Campus Cabedelo, PB 58103-772, Brazil.
Acioli-Santos B; Department of Virology, Aggeu Magalhães Institute (IAM), Oswaldo Cruz Foundation (FIOCRUZ), Recife, PE, Brazil.

Results Phys ; 26: 104306, 2021 Jul.

Article em En | MEDLINE | ID: mdl-34002129

Palavras-chave

COVID-19; Complexity hierarchy; Fisher information measure; Lethality; Permutation entropy; Sliding window technique

Texto completo

Adicionar na Minha BVS

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Results Phys Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Brasil País de publicação: Holanda

Texto completo

Adicionar na Minha BVS

Imprimir

XML

PubMed Links

Buscar no Google