Résumé
High-throughput sequencing has opened the route for a deep assessment of within-host genetic diversity during an infection. This new source of information may be used for the inference of biological processes (e.g. replication mechanisms, host infection process, host-to-host transmission) that are related to the observed level of diversity. However, the evaluation of the performance of the methods that are used for such inference often requires computer-intensive simulations under various demographic, genetic or sampling assumptions. In this context, we built a model that can be simulated to investigate the temporal evolution of genotypes and their frequencies under various scenarios. The model describes the growth and the mutation of genotypes at the nucleotide resolution conditional on an overall within-host viral load dynamics, and can be tuned to generate fast non-equilibrium dynamics. We ran simulations of this model and computed several diversity indices to characterize the temporal variation of within-host genetic diversity (from high-throughput sequences) of virus populations under different infection dynamics. Our results highlight how within-host demography (viral load) and evolution (mutation, selection, or drift) drive variations in within-host diversity during the course of an infection. In particular, we observed a non-monotonic relationship between pathogen population size and genetic diversity. The large variation in the diversity patterns generated in our simulations shows that the underlying model provides a flexible basis to produce very diverse demo-genetic scenarios and test, for instance, methods that use within-host and between-host virus diversity for the inference of transmission links during virus outbreaks.