dc.contributor.author | Nieto-Chaupis, Huber | |
dc.date.accessioned | 2022-04-29T21:55:03Z | |
dc.date.available | 2022-04-29T21:55:03Z | |
dc.date.issued | 2021-11 | |
dc.identifier.citation | Nieto-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.isbn | 978-1-6654-0403-7 | |
dc.identifier.issn | 2693-8421 | |
dc.identifier.uri | https://hdl.handle.net/20.500.13067/1818 | |
dc.description.abstract | At 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.format | application/pdf | es_PE |
dc.language.iso | eng | es_PE |
dc.publisher | Institute of Electrical and Electronics Engineers | es_PE |
dc.rights | info:eu-repo/semantics/restrictedAccess | es_PE |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | es_PE |
dc.source | AUTONOMA | es_PE |
dc.subject | Geometry | es_PE |
dc.subject | Pandemics | es_PE |
dc.subject | Shape | es_PE |
dc.subject | Computational modeling | es_PE |
dc.subject | Geometric modeling | es_PE |
dc.subject | Stochastic processes | es_PE |
dc.subject | Probabilistic logic | es_PE |
dc.title | Geometrical Schemes as Probabilistic and Entropic Tools to Estimate Duration and Peaks of Pandemic Waves | es_PE |
dc.type | info:eu-repo/semantics/article | es_PE |
dc.identifier.journal | 2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) | es_PE |
dc.identifier.doi | https://doi.org/10.1109/SNPD51163.2021.9704981 | |
dc.subject.ocde | https://purl.org/pe-repo/ocde/ford#2.02.04 | es_PE |
dc.publisher.country | PE | es_PE |
dc.relation.url | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125737695&doi=10.1109%2fSNPD51163.2021.9704981&partnerID=40& | es_PE |
dc.source.beginpage | 86 | es_PE |
dc.source.endpage | 89 | es_PE |