Integrating genealogical and dynamical modelling to infer escape and reversion rates in HIV epitopes

TL;DR

This paper introduces an integrated genealogical and epidemiological modeling approach to more accurately estimate HIV escape and reversion rates, revealing significant uncertainty and the importance of sampling strategies.

Contribution

It combines genealogical inference with epidemiological models to improve estimation of HIV escape and reversion rates from population data.

Findings

Uncertainty in escape and reversion rates is substantial due to stochastic evolution.

Genealogical structure can cause estimates to differ several-fold from previous models.

Denser and longitudinal sampling improves parameter estimation accuracy.

Abstract

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.