Uncertainty quantification in modeling HIV viral mechanics.
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.
Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212, United States. htbanks@eos.ncsu.edu.
H T
HT
Banks
Robert
R
Baraldi
Karissa
K
Cross
Kevin
K
Flores
Christina
C
McChesney
Laura
L
Poag
Emma
E
Thorpe
D000465:Algorithms; D019380:Anti-HIV Agents / Q000627:therapeutic use; D015496:CD4-Positive T-Lymphocytes / Q000821:virology; D002986:Clinical Trials as Topic; D015658:HIV Infections / Q000188:drug therapy / Q000503:physiopathology* / Q000821:virology*; D015497:HIV-1 / Q000502:physiology*; D006801:Humans; D016018:Least-Squares Analysis; D008954:Models, Biological; D015233:Models, Statistical; D012367:RNA, Viral / Q000032:analysis; D018894:Reverse Transcriptase Inhibitors / Q000627:therapeutic use; D035501:Uncertainty; D019562:Viral Load
D019380:Anti-HIV Agents; D012367:RNA, Viral; D018894:Reverse Transcriptase Inhibitors