title: Phylodynamics with Migration: A Computational Framework to Quantify Population Structure from Genomic Data.
abstract: When viruses spread, outbreaks can be spawned in previously unaffected regions. Depending on the time and mode of introduction, each regional outbreak can have its own epidemic dynamics. The migration and phylodynamic processes are often intertwined and need to be taken into account when analyzing temporally and spatially structured virus data. In this article, we present a fully probabilistic approach for the joint reconstruction of phylodynamic history in structured populations (such as geographic structure) based on a multitype birth-death process. This approach can be used to quantify the spread of a pathogen in a structured population. Changes in epidemic dynamics through time within subpopulations are incorporated through piecewise constant changes in transmission parameters.We analyze a global human influenza H3N2 virus data set from a geographically structured host population to demonstrate how seasonal dynamics can be inferred simultaneously with the phylogeny and migration process. Our results suggest that the main migration path among the northern, tropical, and southern region represented in the sample analyzed here is the one leading from the tropics to the northern region. Furthermore, the time-dependent transmission dynamics between and within two HIV risk groups, heterosexuals and injecting drug users, in the Latvian HIV epidemic are investigated. Our analyses confirm that the Latvian HIV epidemic peaking around 2001 was mainly driven by the injecting drug user risk group.
authors.affiliation: Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland Department of Computer Science, University of Auckland, Auckland, New Zealand Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland Swiss Institute of Bioinformatics, Lausanne, Switzerland denise.kuehnert@env.ethz.ch.
authors.forename: Denise
authors.initials: D
authors.lastname: Kühnert
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland Swiss Institute of Bioinformatics, Lausanne, Switzerland.
authors.forename: Tanja
authors.initials: T
authors.lastname: Stadler
authors.affiliation: Department of Computer Science, University of Auckland, Auckland, New Zealand.
authors.forename: Timothy G
authors.initials: TG
authors.lastname: Vaughan
authors.affiliation: Department of Computer Science, University of Auckland, Auckland, New Zealand.
authors.forename: Alexei J
authors.initials: AJ
authors.lastname: Drummond
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D004196:Disease Outbreaks; D058872:Epidemics; D023281:Genomics / Q000379:methods; D006801:Humans; D053122:Influenza A Virus, H3N2 Subtype / Q000235:genetics / Q000302:isolation & purification*; D007251:Influenza, Human / Q000453:epidemiology* / Q000635:transmission / Q000821:virology*; D010802:Phylogeny; D058974:Phylogeography / Q000379:methods
keywords: Bayesian inference; biogeography; birth-death model; epidemiology; infectious diseases; phylogeography