title: Modelling the impact of acute infection dynamics on the accumulation of HIV-1 mutations.
abstract: Events over the past year have brought hope and have re-energized the interest in targeting pre-infection or early infection period with preventative or therapeutic interventions such as vaccines and pre-exposure prophylaxis (PrEP). In breakthrough infections, the incidence, long term prognosis and clinical significance of early infection events is not well understood but it is possible that these early events may be crucial in determining the subsequent course of disease. We use a branching process model in a deterministically varying environment to explore how the dynamics of early infection affects the accumulation of mutations which lay the seeds for long term evolution of drug resistance and immune system evasion. We relate this exploration to regimes of impact, on diversity, of tropical interventions strategies such as PrEP and vaccines. As a metric of diversity we compute the probability of existence of particular genomes which potentially arise. Using several model scenarios, we demonstrate various regimes of 'response' of evolution to 'intervention'. Transient effects of therapeutic interventions early in infection that impose a fitness cost on early viruses can significantly reduce the probability of diversity later during the chronic state of infection. This stands in contrast to the concern that early selective pressure may increase the probability of later existence of drug resistance mutations, for example. The branching process paradigm offers the ability to efficiently compute important indicators of viral diversity, in a framework with a modest number of simplifying assumptions, without simulating the full range of individual level scenarios. These models may be useful to illustrate the impact of vaccines and PrEP on viral evolution in the case of breakthrough infection. They also suggest that new measures of viral diversity which correlate to prognosis should be sought in trials for PrEP and vaccines.
authors.affiliation: School of Computational and Applied Mathematics (CAM), University of the Witwatersrand, Johannesburg, South Africa. tshiri@dimacs.rutgers.edu
authors.forename: Tinevimbo
authors.initials: T
authors.lastname: Shiri
authors.forename: Alex
authors.initials: A
authors.lastname: Welte
mesh_terms: D016915:AIDS Vaccines / Q000276:immunology; D050936:Basic Reproduction Number; D003198:Computer Simulation; D015658:HIV Infections / Q000453:epidemiology* / Q000276:immunology / Q000517:prevention & control / Q000821:virology*; D015497:HIV-1 / Q000235:genetics* / Q000502:physiology; D008954:Models, Biological; D009154:Mutation / Q000235:genetics*; D013269:Stochastic Processes; D014779:Virus Replication / Q000502:physiology
chemical_list: D016915:AIDS Vaccines