Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Uncertainty quantification in modeling HIV viral mechanics

1. Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212
2. Center for Research in Scienti c Computation, North Carolina State University, Raleigh, NC 27695-8212

We consider an in-host model for HIV-1 infection dynamics developed and validated with patient data in earlier work [7]. We revisit the earlier model in light of progress over the last several years in understanding HIV-1 progression in humans. We then consider statistical models to describe the data and use these with residual plots in generalized least squares problems to develop accurate descriptions of the proper weights for the data. We use recent parameter subset selection techniques [5,6] to investigate the impact of estimated parameters on the corresponding selection scores. Bootstrapping and asymptotic theory are compared in the context of confidence intervals for the resulting parameter estimates.
  Figure/Table
  Supplementary
  Article Metrics

Keywords bootstrapping.; uncertainty quantification; parameter subset selection; asymptotic distributions; In-host HIV-1 progression models

Citation: H. T. Banks, Robert Baraldi, Karissa Cross, Kevin Flores, Christina McChesney, Laura Poag, Emma Thorpe. Uncertainty quantification in modeling HIV viral mechanics. Mathematical Biosciences and Engineering, 2015, 12(5): 937-964. doi: 10.3934/mbe.2015.12.937

References

  • 1. North American Journal of Medical Sciences, 3 (2011), 499-502.
  • 2. Center for Research in Scientific Computation Technical Report CRSC-TR05-40, NC State Univ., October, 2005; Bulletin of Math. Biology, 69 (2007), 563-584.
  • 3. Current HIV Research, 6 (2008), 388-400.
  • 4. Program and Abstracts of the 20 Conference on Retroviruses and Opportunistic Infections, Atlanta, GA, Abstract No. 47, 2013.
  • 5. J. Inverse and Ill-posed Problems, 17 (2009), 545-564.
  • 6. in Mathematical Modeling and Validation in Physiology, ser. Lecture Notes in Mathematics (eds. J. J. Batzel, M. Bachar, and F. Kappel), Springer Berlin Heidelberg, 2064 (2013), 43-73.
  • 7. Journal of Biological Dynamics, 2 (2008), 357-385.
  • 8. Mathematical and Computer Modeling, 52 (2010), 1610-1625.
  • 9. CRSC Press/ Taylor and Frances Publishing, Boca Raton, FL, 2014.
  • 10. Journal of Biological Dynamics, 7 (2013), 96-132.
  • 11. Taylor Francis/CRC Press, Boca Raton, FL, 2009.
  • 12. Annual Review of Medicine, 53 (2002), 557-593.
  • 13. Chapman & Hall, New York, 1988.
  • 14. J. Amer. Statistical Assoc., 83 (1988), 1045-1054.
  • 15. Journal of Immunology, 186 (2011), 2106-2116.
  • 16. Cellular and Molecular Life Sciences: CMLS, 70 (2013), 3355-3363.
  • 17. PloS one, 3 (2008), {e3907}.
  • 18. ST 762 Lecture Notes, Chapters 2, 3, 9 and 11, 2007; http://www4.stat.ncsu.edu/ davidian/courses.html
  • 19. Chapman and Hall, London, 2000.
  • 20. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 54 (2012), 1495-1503.
  • 21. Statistical Science, 11 (1996), 189-228.
  • 22. CBMS 38, SIAM Publishing, Philadelphia, PA, 1982.
  • 23. Lancet, 360 (2002), 119-129.
  • 24. Nature Medicine, 1 (1995), 129-134.
  • 25. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 41 (2005), 1053-1056.
  • 26. Archives of Internal Medicine, 172 (2012), 1251-1255.
  • 27. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 30 (2000), S96-S116.
  • 28. Retrovirology, 7 (2010), p54.
  • 29. AIDS, 15 (2001), 665-673.
  • 30. Physica D: Nonlinear Phenomena, 142 (2000), 346-382.
  • 31. J. Wiley & Sons, Hoboken, NJ, 1989.
  • 32. Immunity, 36 (2012), 491-501.
  • 33. Chapman and Hall, London, 1986.
  • 34. PLoS Pathogens, 9 (2013), e1003259.
  • 35. Wiley, New York, 2003.
  • 36. Bioinformatics, 18 (2002), S231-S240.
  • 37. The Journal of Infectious Diseases, 191 (2005), 1410-1418.
  • 38. PLoS Pathogens, 8 (2012), e1002352.
  • 39. Immunity, 35 (2011), 161-168.

 

This article has been cited by

  • 1. Mami T. Wentworth, Ralph C. Smith, H. T. Banks, Parameter Selection and Verification Techniques Based on Global Sensitivity Analysis Illustrated for an HIV Model, SIAM/ASA Journal on Uncertainty Quantification, 2016, 4, 1, 266, 10.1137/15M1008245
  • 2. H.T. Banks, Shuhua Hu, Eric Rosenberg, A dynamical modeling approach for analysis of longitudinal clinical trials in the presence of missing endpoints, Applied Mathematics Letters, 2017, 63, 109, 10.1016/j.aml.2016.07.002
  • 3. Arturo Blazquez-Navarro, Thomas Schachtner, Ulrik Stervbo, Anett Sefrin, Maik Stein, Timm H. Westhoff, Petra Reinke, Edda Klipp, Nina Babel, Avidan U. Neumann, Michal Or-Guil, Becca Asquith, Differential T cell response against BK virus regulatory and structural antigens: A viral dynamics modelling approach, PLOS Computational Biology, 2018, 14, 5, e1005998, 10.1371/journal.pcbi.1005998
  • 4. John Lagergren, Amanda Reeder, Franz Hamilton, Ralph C. Smith, Kevin B. Flores, Forecasting and Uncertainty Quantification Using a Hybrid of Mechanistic and Non-mechanistic Models for an Age-Structured Population Model, Bulletin of Mathematical Biology, 2018, 80, 6, 1578, 10.1007/s11538-018-0421-7

Reader Comments

your name: *   your email: *  

Copyright Info: 2015, H. T. Banks, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved