HIV model parameter estimates from interruption trial data
Date
2012
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Delaware
Abstract
Mathematical models based on ordinary differential equations (ODE) have had
signifcant impact on understanding HIV disease dynamics and optimizing patient
treatment. A model that characterizes the essential disease dynamics can be used
for prediction only if the model parameters are identifiable from clinical data. Most
previous studies involved in parameter identification for HIV have used sparse data
from the decay phase following the introduction of therapy. In this thesis, model parameters
are identified from frequently sampled viral-load data taken from ten patients
enrolled in the previously published AutoVac HAART interruption study, providing between
69 and 114 viral load measurements from 3-5 phases of viral decay and rebound
for each patient. This dataset is considerably larger than those used in previously
published parameter estimation studies. Furthermore, the measurements come from
two separate experimental conditions, which allows for the direct estimation of drug
efficacy and reservoir contribution rates, two parameters that cannot be identified from
decay-phase data alone. A Markov-Chain Monte-Carlo method is used to estimate the
model parameter values, with initial estimates obtained using nonlinear least-squares
methods. The posterior distributions of the parameter estimates are reported and
compared for all patients.