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dc.creatorZanin, Massimiliano
dc.creatorPapo, David
dc.date.accessioned2020-07-27T22:00:32Z
dc.date.available2020-07-27T22:00:32Z
dc.date.created2020-09
dc.identifier.issn0960-0779spa
dc.identifier.otherhttps://www.sciencedirect.com/science/article/pii/S0960077920303921?via%3Dihubspa
dc.identifier.urihttp://hdl.handle.net/20.500.12010/11246
dc.format.extent8 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.publisherChaos, Solitons and Fractalseng
dc.sourcereponame:Expeditio Repositorio Institucional UJTLspa
dc.sourceinstname:Universidad de Bogotá Jorge Tadeo Lozanospa
dc.subjectRedes complejasspa
dc.titleAssessing functional propagation patterns in COVID-19spa
dc.type.localArtículospa
dc.subject.lembSíndrome respiratorio agudo gravespa
dc.subject.lembCOVID-19spa
dc.subject.lembSARS-CoV-2spa
dc.subject.lembCoronavirusspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.subject.keywordComplex networksspa
dc.subject.keywordCausalityspa
dc.subject.keywordTime seriesspa
dc.identifier.doihttps://doi.org/10.1016/j.chaos.2020.109993spa
dc.description.abstractenglishAmong the many efforts done by the scientific community to help coping with the COVID-19 pandemic, one of the most important has been the creation of models to describe its propagation, as these are expected to guide the deployment of containment and health policies. These models are commonly based on exogenous information, as e.g. mobility data, whose limitedness always compromise the reliability of obtained results. In this contribution we propose a different approach, based on extracting relationships between the evolution of the disease in different regions through information theoretical metrics. In a way similar to what is commonly done in neuroscience, propagation is understood as information transfer, and the resulting propagation patterns are represented and studied as functional networks. By applying this methodology to the dynamics of COVID-19 in several countries and regions thereof, we were able to reconstruct static and time-varying propagation graphs. We further discuss the advantages, promises and open research questions associated with this functional approach.spa


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