  In this work we develop a stochastic model of acute HIV infection, based on
the well-known standard model, that allows us to simulate the complex mutation
pathways of HIV escape from multiple CTL responses. Under this model, we
describe two computational inference methods. In one, we use a Bayesian
approach to construct posteriors for the parameters of our model. In the
second, we use hypothesis testing to determine the fit of the model to data.
The methods are applied to two CHAVI datasets, demonstrating the importance of
accounting for the interaction of multiple mutant variants and
multi-directional selection in analysing HIV dynamics under CTL response.
