Bayesian Phylodynamic Inference of Multitype Population Trajectories Using Genomic Data

Phylodynamic methods provide a coherent framework for the inference of population parameters directly from genetic data. They are an important tool for understanding both the spread of epidemics as well as long-term macroevolutionary trends in speciation and extinction. In particular, phylodynamic methods based on multitype birth-death models have been used to infer the evolution of discrete traits, the movement of individuals or pathogens between geographic locations or host types, and the transition of infected individuals between disease stages. In these models, population heterogeneity is treated by assigning individuals to different discrete types. Typically, methods which allow inference of parameters under multitype birth-death models integrate over the possible birth-death trajectories (i.e. the type-specific population size functions) to reduce the computational demands of the inference. As a result, it has not been possible to use these methods to directly infer the dynamics of trait-specific population sizes, infected host counts or other such demographic quantities. In this article, we present a method which infers these multitype trajectories with minimal additional computational cost beyond that of existing methods. We demonstrate the practicality of our approach by applying it to a previously published set of Middle East respiratory syndrome coronavirus genomes, inferring the numbers of human and camel cases through time, together with the number and timing of spillovers from the camel reservoir. This application highlights the multitype population trajectory's ability to elucidate properties of the population which are not directly ancestral to its sampled members.