# American Institute of Mathematical Sciences

2015, 12(5): 937-964. doi: 10.3934/mbe.2015.12.937

## 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, United States, United States, United States, United States, United States, United States

Received  January 2014 Revised  April 2015 Published  June 2015

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. Mathematical Biosciences & Engineering, 2015, 12 (5) : 937-964. doi: 10.3934/mbe.2015.12.937
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##### References:
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