Computational uncertainty quantification for random time‐discrete epidemiological models using adaptive gPC
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Scholar |
Otros documentos de la autoría: Calatayud, Julia; Cortés, Juan Carlos; Jornet, Marc; Villanueva, Rafael-Jacinto
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https://doi.org/10.1002/mma.5315 |
Metadatos
Título
Computational uncertainty quantification for random time‐discrete epidemiological models using adaptive gPCFecha de publicación
2018-10-22Editor
John Wiley & Sons, Ltd.ISSN
0170-4214; 1099-1476Cita bibliográfica
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.5315Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://onlinelibrary.wiley.com/doi/10.1002/mma.5315Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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 determi ... [+]
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. [-]
Publicado en
Mathematical Methods in the Applied Sciences, Vol. 41, Iss.18. Special Issue: Biomathematics/Advanced Analysis in Pure & Applied Sciences (Decembre 2018)Entidad financiadora
Ministerio de Economía y Competitividad | Universitat Politècnica de València
Código del proyecto o subvención
MTM2017–89664–P
Título del proyecto o subvención
Programa de Ayudas de Investigación y Desarrollo (PAID)
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© 2018 John Wiley & Sons, Ltd.
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