Going back to the roots: Evaluating Bayesian phylogeographic models with discrete trait uncertainty.

Going back to the roots: Evaluating Bayesian phylogeographic models with discrete trait uncertainty. Infect Genet Evol. 2020 Aug 12;:104501 Authors: Vaiente MA, Scotch M Abstract Phylogeography is a popular way to analyze virus sequences annotated with discrete, epidemiologically-relevant, trait data. For applied public health surveillance, a key quantity of interest is often the state at the root of the inferred phylogeny. In epidemiological terms, this represents the geographic origin of the observed outbreak. Since determining the origin of an outbreak is often critical for public health intervention, it is prudent to understand how well phylogeographic models perform this root state classification task under various analytical scenarios. Specifically, we investigate how discrete state space and sequence data set influence the root state classification accuracy. We performed phylogeographic inference on several simulated DNA data sets while i) increasing the number of sequences and ii) increasing the total number of possible discrete trait values. We show that phylogeographic models tend to perform best at intermediate sequence data set sizes. Further, we demonstrate that a popular metric used for evaluation of phylogeographic models, the Kullback-Leibler (KL) divergence, both increases with discrete state space and data set sizes. Further, by modeling phylogeographic root state classification accuracy using logistic regression, w...
Source: Infection, Genetics and Evolution - Category: Genetics & Stem Cells Authors: Tags: Infect Genet Evol Source Type: research