RNA viruses yield the highest mutation rates of all groups of pathogens which is approximately six orders of magnitude higher than in most DNA organisms [23, 26] . In the context of viral population genetics, substitution or evolutionary rates can be defined as the number of fixed mutational changes that accumulate in the population per nucleotide site per unit of time [27] . This rate is driven by the short-generation times of viruses and their error-prone RNA polymerase proteins lacking proofreading activity. Combined with their small genomes, these characteristics make RNA virus ideal models for evolutionary research [23, 28, 29] . In addition, recombination events may also play a role in RNA virus evolution [23] . While lacking a fossil record, evolutionary histories of RNA viruses can be calibrated because they represent 'measurably evolving populations', in which genetic diversity accumulates over a timescale of human observation [30] . Their evolutionary history and population dynamics can be reconstructed by means of genealogy-based coalescent approaches using nucleotide sequences sampled over an epidemiological time frame in order to estimate timed viral ancestry as well as the rates of genetic change [27, 29] . The most advanced methods operating on time-stamped sequence data use Bayesian Metropolis-Hastings Markov Chain Monte-Carlo (MCMC) algorithms that accommodate for the uncertainty of phylogenies rooted in time. Here, we estimated the substitution rates for the P1 and VP1 regions of HPeV with such a Bayesian approach, which provides a statistical framework for evolutionary analysis [31] .