DOI: 10.5061/dryad.7jr85rj
title: Data from: Incorporating sampling uncertainty in the geospatial assignment of taxa for virus phylogeography

description: 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% of the taxa had different amounts of
 sampling uncertainty including 10%, 30%, and 50% 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 pre-defined heuristic approaches: assignment to a
 centroid location (CNTR) or the largest population in the country (POP).
 We compared all scenarios to a reference standard in which all taxa had
 known (absolutely certain) locations. From this, we implemented five
 random selections of 25% 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 reference standard 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 reference standard 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 web interface for
 researchers to use our language processing methods to find and assign
 uncertainty to alternative potential locations for virus phylogeography.
description: H5N1
 MCCsMaximum Clade
 Credibility (MCC) Trees for each model/sample of H5N1 in
 Egypt.H5N1_MCCs.zippdm09 MCCsMaximum Clade Credibility (MCC) Trees for each model/sample of pdm09 in Mexico and USA.pdm09_MCCs.zipEmpirical Tree for H5N1 MCMCsStarting empirical tree used for H5N1 MCMCs. Use this for the H5N1 BEAST XMLs.H5N1_1K.treesEmpirical Trees for pdm09 MCMCsStarting empirical trees used for pdm09 MCMCs. Use this for the pdm09 BEAST XMLs.pdm09_1K.treesH5N1 XMLs41 XMLs for the H5N1 ScenarioH5N1_xmls.zippdm09 XMLs41 XMLs for the pdm09 Scenariopdm09_xmls.zip