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Phylodynamics with Migration: A Computational Framework to Quantify Population Structure from Genomic Data.

Denise Kühnert¹, Tanja Stadler², Timothy G Vaughan³

¹Department 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.

Molecular Biology and Evolution

|May 19, 2016

Summary

This summary is machine-generated.

This study introduces a probabilistic model to track virus spread and evolution in structured populations. It reveals key migration routes for influenza H3N2 and identifies injecting drug users as the primary drivers of the Latvian HIV epidemic.

Keywords:

Bayesian inference biogeography birth-death model epidemiology infectious diseases phylogeography

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Published on: August 14, 2018

Area of Science:

Epidemiology
Viral Phylodynamics
Computational Biology

Background:

Virus outbreaks can emerge in new regions with unique epidemic dynamics.
Analyzing virus spread requires considering intertwined migration and phylodynamic processes in structured populations.
Understanding pathogen transmission across populations is crucial for public health.

Purpose of the Study:

To develop a probabilistic framework for reconstructing phylodynamic history in structured populations.
To quantify pathogen spread by jointly analyzing migration and evolutionary processes.
To investigate time-varying transmission dynamics within and between subpopulations.

Main Methods:

A fully probabilistic approach using a multitype birth-death process.
Joint reconstruction of phylodynamic history and migration patterns.
Incorporation of piecewise constant changes in transmission parameters to model epidemic dynamics over time.

Main Results:

Inferred seasonal dynamics, phylogeny, and migration for a global human influenza H3N2 virus dataset.
Identified the primary migration path for H3N2 from tropical to northern regions.
Confirmed that injecting drug users were the main drivers of the Latvian HIV epidemic peaking around 2001.

Conclusions:

The developed probabilistic approach effectively quantifies pathogen spread in structured populations.
Seasonal transmission patterns and migration routes can be simultaneously inferred.
The study provides insights into the drivers of specific epidemics, such as influenza and HIV.