The field of phylodynamics has witnessed a rich development of statistical inference tools with increasing levels of sophistication that can be applied to address a variety of questions about the evolution and epidemiology of viruses. Many of these inference tools have been implemented in a Bayesian phylogenetic framework that focuses on time-measured trees. To adequately estimate the time-scale of viral evolutionary reconstructions, statistical models are required that can describe complex patterns of sequence evolution over time.
Here, I will discuss recent developments in molecular clock modeling that aim at accommodating complex sources of rate heterogeneity. One motivating example is substitution rate variation among HIV-1 group M subtypes that cannot be appropriately modeled using an uncorrelated relaxed clock model. To address this, I will propose a mixed effects model that combines both fixed (clade-specific) and random effects on the rate of evolution. Another complication for deeper viral evolutionary histories is the time-dependency of evolutionary rates. Motivated by on previous approaches to quantify these rate dynamics, we have developed a Bayesian ‘epoch’ modeling approach that models this time-dependency in an unknown phylogeny. Finally, I will discuss how these concepts can be combined for Hepatitis B virus, which represents an example of a deep evolutionary history that has been difficult to date accurately using sequences sampled over the last decades. Recent ancient DNA work has resulted in the first HBV samples dating back thousands of years. I will discuss how our molecular clock modeling efforts can reconcile recent rapid evolutionary rate estimates with the long-term evolutionary dynamics of the virus.