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Mathematical Biosciences and Engineering

2015, Volume 12, Issue 5: 937-964. doi: 10.3934/mbe.2015.12.937
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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
  • Received: 01 January 2014 Accepted: 29 June 2018 Published: 01 June 2015

  • MSC : 62F12, 62F40.

  • 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.

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

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  • 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.


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  • © 2015 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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