Computational uncertainty quantification for random time‐discrete epidemiological models using adaptive gPC
Impact
![Google Scholar](/xmlui/themes/Mirage2/images/uji/logo_google.png)
![Microsoft Academico](/xmlui/themes/Mirage2/images/uji/logo_microsoft.png)
Metadata
Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
comunitat-uji-handle4:
INVESTIGACIONThis resource is restricted
https://doi.org/10.1002/mma.5315 |
Metadata
Title
Computational uncertainty quantification for random time‐discrete epidemiological models using adaptive gPCDate
2018-10-22Publisher
John Wiley & Sons, Ltd.ISSN
0170-4214; 1099-1476Bibliographic 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.5315Type
info:eu-repo/semantics/articlePublisher version
https://onlinelibrary.wiley.com/doi/10.1002/mma.5315Version
info:eu-repo/semantics/publishedVersionSubject
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 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. [-]
Is part of
Mathematical Methods in the Applied Sciences, Vol. 41, Iss.18. Special Issue: Biomathematics/Advanced Analysis in Pure & Applied Sciences (Decembre 2018)Funder Name
Ministerio de Economía y Competitividad | Universitat Politècnica de València
Project code
MTM2017–89664–P
Project title or grant
Programa de Ayudas de Investigación y Desarrollo (PAID)
Rights
© 2018 John Wiley & Sons, Ltd.
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
This item appears in the folowing collection(s)
- MAT_Articles [765]
- INIT_Articles [752]