  The rates of escape and reversion in response to selection pressure arising
from the host immune system, notably the cytotoxic T-lymphocyte (CTL) response,
are key factors determining the evolution of HIV. Existing methods for
estimating these parameters from cross-sectional population data using ordinary
differential equations (ODE) ignore information about the genealogy of sampled
HIV sequences, which has the potential to cause systematic bias and
over-estimate certainty. Here, we describe an integrated approach, validated
through extensive simulations, which combines genealogical inference and
epidemiological modelling, to estimate rates of CTL escape and reversion in HIV
epitopes. We show that there is substantial uncertainty about rates of viral
escape and reversion from cross-sectional data, which arises from the inherent
stochasticity in the evolutionary process. By application to empirical data, we
find that point estimates of rates from a previously published ODE model and
the integrated approach presented here are often similar, but can also differ
several-fold depending on the structure of the genealogy. The model-based
approach we apply provides a framework for the statistical analysis of escape
and reversion in population data and highlights the need for longitudinal and
denser cross-sectional sampling to enable accurate estimate of these key
parameters.
