Using symbolic networks to analyse dynamical properties of disease outbreaks

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dc.contributor.authorHerrera-Diestra, Jose L. [UNESP]
dc.contributor.authorBuldu, Javier M.
dc.contributor.authorChavez, Mario
dc.contributor.authorMartinez, Johann H. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniv Los Andes
dc.contributor.institutionGrp Interdisciplinar Sistemas Complejos GISC
dc.contributor.institutionCtr Biomed Technol UPM
dc.contributor.institutionUniv Rey Juan Carlos
dc.contributor.institutionHop La Pitie Salpetriere
dc.date.accessioned2020-12-10T19:58:17Z
dc.date.available2020-12-10T19:58:17Z
dc.date.issued2020-04-29
dc.description.abstractWe 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.en
dc.description.affiliationUniv Estadual Paulista, Inst Fis Teor, ICTP South Amer Inst Fundamental Res, Sao Paulo, Brazil
dc.description.affiliationUniv Los Andes, CeSiMo, Fac Ingn, Merida, Venezuela
dc.description.affiliationGrp Interdisciplinar Sistemas Complejos GISC, Madrid, Spain
dc.description.affiliationCtr Biomed Technol UPM, Lab Biol Networks, Madrid, Spain
dc.description.affiliationUniv Rey Juan Carlos, Complex Syst Grp, Mostoles, Spain
dc.description.affiliationHop La Pitie Salpetriere, CNRS, UMR7225, Paris, France
dc.description.affiliationUniv Los Andes, Dept Biomed Engn, Bogota, Colombia
dc.description.affiliationUnespUniv Estadual Paulista, Inst Fis Teor, ICTP South Amer Inst Fundamental Res, Sao Paulo, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipMINECO
dc.description.sponsorshipIdFAPESP: 2016/01343-7
dc.description.sponsorshipIdFAPESP: 2017/05770-0
dc.description.sponsorshipIdMINECO: FIS201784151-P
dc.format.extent18
dc.identifierhttp://dx.doi.org/10.1098/rspa.2019.0777
dc.identifier.citationProceedings Of The Royal Society A-mathematical Physical And Engineering Sciences. London: Royal Soc, v. 476, n. 2236, 18 p., 2020.
dc.identifier.doi10.1098/rspa.2019.0777
dc.identifier.issn1364-5021
dc.identifier.urihttp://hdl.handle.net/11449/196854
dc.identifier.wosWOS:000530375100004
dc.language.isoeng
dc.publisherRoyal Soc
dc.relation.ispartofProceedings Of The Royal Society A-mathematical Physical And Engineering Sciences
dc.sourceWeb of Science
dc.subjectcomplex networks
dc.subjectordinal patterns
dc.subjectentropy
dc.subjecttime series
dc.subjectepidemics
dc.titleUsing symbolic networks to analyse dynamical properties of disease outbreaksen
dc.typeArtigo
dcterms.rightsHolderRoyal Soc
unesp.author.orcid0000-0002-3365-8189[4]

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