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dc.contributor.authorNieto-Chaupis, Huber
dc.date.accessioned2022-04-29T21:55:03Z
dc.date.available2022-04-29T21:55:03Z
dc.date.issued2021-11
dc.identifier.citationNieto-Chaupis, H. (2021). Geometrical Schemes as Probabilistic and Entropic Tools to Estimate Duration and Peaks of Pandemic Waves. In 2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 86-89). IEEE.es_PE
dc.identifier.isbn978-1-6654-0403-7
dc.identifier.issn2693-8421
dc.identifier.urihttps://hdl.handle.net/20.500.13067/1818
dc.description.abstractAt the end of first quarter of 2020 it was seen in most countries statistics the beginning of an imminent second wave of pandemic. On January of 2021 it was seen in the data a rapid growth of new infections. In this paper, a geometry-based scheme is presented. In concrete the rectangle and trapezoid shapes are analyzed. From this, a relation between both geometries is extracted in terms of polynomial functions. The resulting characterization of a pandemic in terms of geometric variables is presented. Thus the present model is confronted with official data of USA and India. From the results of this paper, it is strongly believed that entropy might be behind of a global pandemic dynamics.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherInstitute of Electrical and Electronics Engineerses_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/es_PE
dc.sourceAUTONOMAes_PE
dc.subjectGeometryes_PE
dc.subjectPandemicses_PE
dc.subjectShapees_PE
dc.subjectComputational modelinges_PE
dc.subjectGeometric modelinges_PE
dc.subjectStochastic processeses_PE
dc.subjectProbabilistic logices_PE
dc.titleGeometrical Schemes as Probabilistic and Entropic Tools to Estimate Duration and Peaks of Pandemic Waveses_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.journal2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)es_PE
dc.identifier.doihttps://doi.org/10.1109/SNPD51163.2021.9704981
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04es_PE
dc.publisher.countryPEes_PE
dc.relation.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85125737695&doi=10.1109%2fSNPD51163.2021.9704981&partnerID=40&es_PE
dc.source.beginpage86es_PE
dc.source.endpage89es_PE


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