Transient dynamics are frequently observed in biological systems with high levels of stochasticity and slow relaxation times to equilibrium. For viral pathogens, transient dynamics frequently also arise from their rapid evolutionary dynamics. In the case of influenza, dengue, and norovirus, among other RNA viruses, these evolutionary dynamics occur on the same timescale as their ecological dynamics. These viruses’ transient dynamics therefore consist of two distinct types: population dynamic transients and genetic transients. While the importance of transients in disease dynamics has been increasing recognized by theoreticians, quantitative studies of genetic transients are few. These transients can be thought of as an evolving community of viral genotypes over time. Characteristics of this changing community can be quantified through diversity and divergence calculations. Both population dynamic and genetic transients of viral pathogens can guide us in the identification of the processes underlying their ecological and evolutionary dynamics.
Results/Conclusions
In this talk, I will sketch out how these two types of transients can inform our understanding of the processes generating the other. With a simple illustrative model, I first show that genetic transients can help us identify whether disease dynamics are driven by intrinsic dynamics or by extrinsic forcing (or by a combination of extrinsic and intrinsic factors). Conversely, I show with this model that population dynamic transients can help us quantify the selective advantage of new strain variants, which in turn affects standing levels of genetic diversity. Moving from the illustrative model to a more extensive model for influenza, I show that the emergence and replacement of antigenic clusters, along with neutral within-cluster genetic diversification, can reproduce certain key characteristics of influenza’s genetic and population dynamic transients. Confronting this model more quantitatively with observed patterns of genetic variation also improves our understanding of the extent of sequence space constraints to viral evolution.