@article{3385ef97e5c741159df93d981eb3cb35,
title = "Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2",
abstract = "Spatiotemporal bias in genome sampling can severely confound discrete trait phylogeographic inference. This has impeded our ability to accurately track the spread of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, despite the availability of unprecedented numbers of SARS-CoV-2 genomes. Here, we present an approach to integrate individual travel history data in Bayesian phylogeographic inference and apply it to the early spread of SARS-CoV-2. We demonstrate that including travel history data yields i) more realistic hypotheses of virus spread and ii) higher posterior predictive accuracy compared to including only sampling location. We further explore methods to ameliorate the impact of sampling bias by augmenting the phylogeographic analysis with lineages from undersampled locations. Our reconstructions reinforce specific transmission hypotheses suggested by the inclusion of travel history data, but also suggest alternative routes of virus migration that are plausible within the epidemiological context but are not apparent with current sampling efforts.",
author = "Philippe Lemey and Hong, {Samuel L.} and Verity Hill and Guy Baele and Chiara Poletto and Vittoria Colizza and {\'A}ine O{\textquoteright}Toole and McCrone, {John T.} and Andersen, {Kristian G.} and Michael Worobey and Nelson, {Martha I.} and Andrew Rambaut and Suchard, {Marc A.}",
note = "Funding Information: We would like to thank all the authors who have kindly shared genome data on GISAID, and we have included a table (Supplementary Tables 4 and 5) listing the authors and institutes involved. The research leading to these results has received funding from the European Research Council under the European Union{\textquoteright}s Horizon 2020 research and innovation program (grant agreement no. 725422-ReservoirDOCS) and from the European Union{\textquoteright}s Horizon 2020 project MOOD (grant agreement no. 874850). The Artic Network receives funding from the Wellcome Trust through project 206298/Z/17/Z. P.L. acknowledges support by the Research Foundation—Flanders (“Fonds voor Weten-schappelijk Onderzoek—Vlaanderen”, G066215N, G0D5117N, and G0B9317N). G.B. acknowledges support from the Interne Fondsen KU Leuven/Internal Funds KU Leuven under grant agreement C14/18/094, and the Research Foundation—Flanders (“Fonds voor Wetenschappelijk Onderzoek—Vlaanderen”, G0E1420N). M.A.S. and K.G.A. acknowledge support from National Institutes of Health grant U19 AI135995. We also gratefully acknowledge support from NVIDIA Corporation with the donation of parallel computing resources used for this research. This work was supported by the Multinational Influenza Seasonal Mortality Study (MISMS), an on-going international collaborative effort to understand influenza epidemiology and evolution, led by the Fogarty International Center, NIH. The content is solely the responsibility of the authors and does not necessarily represent official views of the National Institutes of Health. Publisher Copyright: {\textcopyright} 2020, The Author(s).",
year = "2020",
month = dec,
day = "1",
doi = "10.1038/s41467-020-18877-9",
language = "English (US)",
volume = "11",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
number = "1",
}