Discrete phylogeography using software such as BEAST considers the sampling location of each taxon as fixed; often to a single location without uncertainty. When studying viruses, this implies that there is no possibility that the location of the infected host for that taxa is somewhere else. Here, we relaxed this strong assumption and allowed for analytic integration of uncertainty for discrete virus phylogeography. We used automatic language processing methods to find and assign uncertainty to alternative potential locations. We considered two influenza case studies: H5N1 in Egypt; H1N1 pdm09 in North America. For each, we implemented scenarios in which 25 per cent of the taxa had different amounts of sampling uncertainty including 10, 30, and 50 per cent uncertainty and varied how it was distributed for each taxon. This includes scenarios that: (i) placed a specific amount of uncertainty on one location while uniformly distributing the remaining amount across all other candidate locations (correspondingly labeled 10 , 30 , and 50 ); (ii) assigned the remaining uncertainty to just one other location; thus ‘splitting’ the uncertainty among two locations (i.e. 10/90, 30/70 , and 50/50 ); and (iii) eliminated uncertainty via two predefined heuristic approaches: assignment to a centroid location (CNTR) or the largest population in the country (POP). We compared all scenarios to a reference standard (RS) in which all taxa had known (absolutely certain) locations. From this, we implemented five random selections of 25 per cent of the taxa and used these for specifying uncertainty. We performed posterior analyses for each scenario, including: (a) virus persistence, (b) migration rates, (c) trunk rewards, and (d) the posterior probability of the root state. The scenarios with sampling uncertainty were closer to the RS than CNTR and POP. For H5N1, the absolute error of virus persistence had a median range of 0.005–0.047 for scenarios with sampling uncertainty—(i) and (ii) above—versus a range of 0.063–0.075 for CNTR and POP. Persistence for the pdm09 case study followed a similar trend as did our analyses of migration rates across scenarios (i) and (ii). When considering the posterior probability of the root state, we found all but one of the H5N1 scenarios with sampling uncertainty had agreement with the RS on the origin of the outbreak whereas both CNTR and POP disagreed. Our results suggest that assigning geospatial uncertainty to taxa benefits estimation of virus phylogeography as compared to ad-hoc heuristics. We also found that, in general, there was limited difference in results regardless of how the sampling uncertainty was assigned; uniform distribution or split between two locations did not greatly impact posterior results. This framework is available in BEAST v.1.10. In future work, we will explore viruses beyond influenza. We will also develop a w...

GenBank is a popular National Center for Biotechnology Information (NCBI) database for submission and analysis of DNA sequences for biomedical research. The resource is part of the Entrez environment which enables for cross-linking of concepts and entries in other participating NCBI databases such as Taxonomy, PubMed and Protein. For example, a GenBank record of an influenza A hemagglutinin gene DNA sequence might have a link to the Taxonomy database for the organism, a link to the related article in PubMed (if published) and a link to the Protein entry for the hemagglutinin protein. Despite its importance in biomedical research such as population genetics, phylogeography and public health surveillance, the host and geospatial metadata of genetic sequences in GenBank are not linked to any database. Therefore, to facilitate biomedical research based on georeferenced DNA sequences and/or DNA sequences with normalized host names, we designed and developed a framework that enriches GenBank entries by linking their host metadata to the NCBI Taxonomy database and their geospatial metadata to a comprehensive knowledge base of geographic locations called GeoNames. Here, we introduce a database created through the application of this framework to virus sequences in GenBank, and evaluate our normalization algorithms on a set of manually annotated records pertaining to viruses. Although currently applied to viruses, our framework can be easily extended to other organisms, and we discuss the potential utilization of our resource for biomedical research. Database URL: https://zodo.asu.edu/zoophydb/

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, we show that KL is not supported as a predictor of model accuracy, indicating its limited utility for assessing phylogeographic model performance on empirical data. These results suggest that relying solely on the KL metric may lead to artificially inflated support for models with finer discretization schemes and larger data set sizes. These results will be important for public health practitioners seeking to use phylogeographic models for applied infectious disease surveillance.