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Computational and Mathematical Methods in Medicine
Volume 2012, Article ID 742086, 8 pages
Research Article

Uncertainty Quantification in Simulations of Epidemics Using Polynomial Chaos

1Department of Statistics and Operational Research, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Valencia, Spain
2Department of Mathematics, University of Texas at Arlington, Arlington, TX 76019-0408, USA

Received 8 May 2012; Revised 25 June 2012; Accepted 3 July 2012

Academic Editor: Thierry Busso

Copyright © 2012 F. Santonja and B. Chen-Charpentier. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [5 citations]

The following is the list of published articles that have cited the current article.

  • Frederico Martins Alves da Silva, Augusta Finotti Brazão, and Paulo Batista Gonçalves, “Influence of Physical and Geometrical Uncertainties in the Parametric Instability Load of an Axially Excited Cylindrical Shell,” Mathematical Problems in Engineering, vol. 2015, pp. 1–18, 2015. View at Publisher · View at Google Scholar
  • David B. Harman, and Peter R. Johnston, “Applying the stochastic Galerkin method to epidemic models with uncertainty in the parameters,” Mathematical Biosciences, vol. 277, pp. 25–37, 2016. View at Publisher · View at Google Scholar
  • Ruoyan Sun, “Optimal weight based on energy imbalance and utility maximization,” Physica A-Statistical Mechanics And Its Applications, vol. 442, pp. 429–435, 2016. View at Publisher · View at Google Scholar
  • Julia Calatayud, Juan Carlos Cortés, Marc Jornet, and Rafael Jacinto Villanueva, “Computational uncertainty quantification for random time-discrete epidemiological models using adaptive gPC,” Mathematical Methods in the Applied Sciences, 2018. View at Publisher · View at Google Scholar
  • Julia Calatayud Gregori, Benito Chen-Charpentier, Juan Cortés López, and Marc Jornet Sanz, “Combining Polynomial Chaos Expansions and the Random Variable Transformation Technique to Approximate the Density Function of Stochastic Problems, Including Some Epidemiological Models,” Symmetry, vol. 11, no. 1, pp. 43, 2019. View at Publisher · View at Google Scholar