Using symbolic networks to analyse dynamical properties of disease outbreaks.

complex networks entropy epidemics ordinal patterns time series

Journal

Proceedings. Mathematical, physical, and engineering sciences
ISSN: 1364-5021
Titre abrégé: Proc Math Phys Eng Sci
Pays: England
ID NLM: 9891746

Informations de publication

Date de publication:
Apr 2020
Historique:
received: 09 11 2019
accepted: 19 03 2020
entrez: 14 5 2020
pubmed: 14 5 2020
medline: 14 5 2020
Statut: ppublish

Résumé

We introduce a new methodology, which is based on the construction of epidemic networks, to analyse the evolution of epidemic time series. First, we translate the time series into ordinal patterns containing information about local fluctuations in disease prevalence. Each pattern is associated with a node of a network, whose (directed) connections arise from consecutive appearances in the series. The analysis of the network structure and the role of each pattern allows them to be classified according to the enhancement of entropy/complexity along the series, giving a different point of view about the evolution of a given disease.

Identifiants

pubmed: 32398936
doi: 10.1098/rspa.2019.0777
pii: rspa20190777
pmc: PMC7209146
doi:

Types de publication

Journal Article

Langues

eng

Pagination

20190777

Informations de copyright

© 2020 The Author(s).

Déclaration de conflit d'intérêts

We declare we have no competing interests.

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Auteurs

José L Herrera-Diestra (JL)

ICTP South American Institute for Fundamental Research, Instituto de Física Teórica, Universidade Estadual Paulista, São Paulo, Brazil.
CeSiMo, Facultad de Ingeniería, Universidad de Los Andes, Mérida, Venezuela.

Javier M Buldú (JM)

Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain.
Laboratory of Biological Networks, Center for Biomedical Technology - UPM, Madrid, Spain.
Complex Systems Group, Universidad Rey Juan Carlos, Móstoles, Spain.

Mario Chavez (M)

CNRS UMR7225, Hôpital Pitié Salpêtrière, Paris, France.

Johann H Martínez (JH)

ICTP South American Institute for Fundamental Research, Instituto de Física Teórica, Universidade Estadual Paulista, São Paulo, Brazil.
Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain.
Department of Biomedical Engineering, Universidad de los Andes, Bogotá, Colombia.

Classifications MeSH