Bayesian estimation of HIV-1 dynamics in vivo.
Statistical analysis of viral dynamics in HIV-1 infected patients undergoing structured treatment interruptions were performed using a novel model that accounts for treatment efficiency as well as total CD8+ T cell counts. A brief review of parameter estimates obtained in other studies is given, pointing to a considerable variation in the estimated values. A Bayesian approach to parameter estimation was used with longitudinal measurements of CD4+ and CD8+ T cell counts and HIV RNA. We describe an estimation procedure which uses spline approximations of CD8+ T cells dynamics. This approach reduces the number of parameters that must be estimated and is especially helpful when the CD8+ T cells growth function has a delayed dependence on the past. Seven important parameters related to HIV-1 in-host dynamics were estimated, most of them treated as global parameters across the group of patients. The estimated values were mainly in keeping with the estimates obtained in other reports, but our paper also introduces the estimates of some new parameters which supplement the current knowledge. The method was also tested on a simulated data set.
Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway anastasi@alumni.ntnu.no.
Anastasia
A
Ushakova
Department of Infectious Diseases, Oslo University Hospital, Oslo, Norway.
Frank Olav
FO
Pettersen
Department of Infectious Diseases, Oslo University Hospital, Oslo, Norway.
Arild
A
Mæland
Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
Bo Henry
BH
Lindqvist
Department of Infectious Diseases, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
Dag
D
Kvale
D000465:Algorithms; D023241:Antiretroviral Therapy, Highly Active; D001499:Bayes Theorem; D018791:CD4 Lymphocyte Count; D018414:CD8-Positive T-Lymphocytes; D015658:HIV Infections / Q000188:drug therapy / Q000276:immunology / Q000821:virology*; D015497:HIV-1; D054884:Host-Pathogen Interactions; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D055641:Mathematical Concepts; D008954:Models, Biological; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D012367:RNA, Viral / Q000097:blood
D012367:RNA, Viral
HIV-1; MCMC; parameter estimation; structured treatment interruptions