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Computational uncertainty quantification for random time‐discrete epidemiological models using adaptive gPC
dc.contributor.author | Calatayud, Julia | |
dc.contributor.author | Cortés, Juan Carlos | |
dc.contributor.author | Jornet, Marc | |
dc.contributor.author | Villanueva, Rafael-Jacinto | |
dc.date.accessioned | 2022-11-30T14:42:33Z | |
dc.date.available | 2022-11-30T14:42:33Z | |
dc.date.issued | 2018-10-22 | |
dc.identifier.citation | Calatayud, J, Cortés, JC, Jornet, M, Villanueva, RJ. Computational uncertainty quantification for random time-discrete epidemiological models using adaptive gPC. Math Meth Appl Sci. 2018; 41: 9618– 9627. https://doi.org/10.1002/mma.5315 | ca_CA |
dc.identifier.issn | 0170-4214 | |
dc.identifier.issn | 1099-1476 | |
dc.identifier.uri | http://hdl.handle.net/10234/200999 | |
dc.description.abstract | Population dynamics models consisting of nonlinear difference equations allow us to get a better understanding of theprocesses involved in epidemiology. Usually, these mathematical models are studied under a deterministic approach.However, in order to take into account the uncertainties associated with the measurements of the model input param-eters, a more realistic approach would be to consider these inputs as random variables. In this paper, we study therandom time-discrete epidemiological models SIS, SIR, SIRS, and SEIR using a powerful unified approach basedupon the so-called adaptive generalized polynomial chaos (gPC) technique. The solution to these random differenceequations is a stochastic process in discrete time, which represents the number of susceptible, infected, recovered,etc individuals at each time step. We show, via numerical experiments, how adaptive gPC permits quantifying theuncertainty for the solution stochastic process of the aforementioned random time-discrete epidemiological modeland obtaining accurate results at a cheap computational expense. We also highlight how adaptive gPC can be appliedin practice, by means of an example using real data. | ca_CA |
dc.format.extent | 10 p. | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | John Wiley & Sons, Ltd. | ca_CA |
dc.relation | Programa de Ayudas de Investigación y Desarrollo (PAID) | ca_CA |
dc.relation.isPartOf | Mathematical Methods in the Applied Sciences, Vol. 41, Iss.18. Special Issue: Biomathematics/Advanced Analysis in Pure & Applied Sciences (Decembre 2018) | ca_CA |
dc.rights | © 2018 John Wiley & Sons, Ltd. | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | adaptive gPC | ca_CA |
dc.subject | computational methods for stochastic equations | ca_CA |
dc.subject | computational uncertainty quantification | ca_CA |
dc.subject | random nonlinear difference equations model | ca_CA |
dc.subject | random population dynamics model | ca_CA |
dc.subject | random time-discrete epidemiological model | ca_CA |
dc.subject | stochastic difference equations | ca_CA |
dc.title | Computational uncertainty quantification for random time‐discrete epidemiological models using adaptive gPC | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1002/mma.5315 | |
dc.rights.accessRights | info:eu-repo/semantics/restrictedAccess | ca_CA |
dc.relation.publisherVersion | https://onlinelibrary.wiley.com/doi/10.1002/mma.5315 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | Ministerio de Economía y Competitividad | ca_CA |
project.funder.name | Universitat Politècnica de València | ca_CA |
oaire.awardNumber | MTM2017–89664–P | ca_CA |
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