Bayesian phylodynamic inference with complex models

We present a framework for coalescent-based phylogenetic and phylodyn amic inference which enables highly-flexible modeling of demographic and epidemiological processes. This approach builds upon previous structured coalescent approaches and includes enhancements for computational speed, accuracy, and stability. A flexible markup language is described for translating parametric demographic or epidemiological models into a structured coalescent model enabling simultaneous estimation of demographic or epidemiological parameters and time-scaled phylogenies. We demonstrate the utility of these approaches by fitting compartmental epidemiological models to Ebola virus and Influenza A virus sequence data, demonstrating how important features of these epidemics, such as the reproduction number and epidemic curves, can be gleaned from genetic data. These approaches are provided as an open-source packagePhyDyn for the BEAST2 phylogenetics platform.

Source: PLoS Computational Biology - November 13, 2018 Category: Biology Authors: Erik M. Volz Source Type: research