Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic

Abstract

Influenza A viruses cause seasonal epidemics and occasional pandemics in the human population. While the worldwide circulation of seasonal influenza is at least partly understood, the exact migration patterns between countries, states or cities are not well studied. Here, we use the Sankoff algorithm for parsimonious phylogeographic reconstruction together with effective distances based on a worldwide air transportation network. By first simulating geographic spread and then phylogenetic trees and genetic sequences, we confirmed that reconstructions with effective distances inferred phylogeographic spread more accurately than reconstructions with geographic distances and Bayesian reconstructions with BEAST that do not use any distance information, and led to comparable results to the Bayesian reconstruction using distance information via a generalized linear model. Our method extends Bayesian methods that estimate rates from the data by using fine-grained locations like airports and inferring intermediate locations not observed among sampled isolates. When applied to sequence data of the pandemic H1N1 influenza A virus in 2009, our approach correctly inferred the origin and proposed airports mainly involved in the spread of the virus. In case of a novel outbreak, this approach allows to rapidly analyze sequence data and infer origin and spread routes to improve disease surveillance and control.

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research

“…using air travel or local diffusion, to reconstruct the putative spread paths and outbreak origins of rapidly evolving pathogens. [ 82 ] published a method that infers locations for internal nodes of a phylogenetic tree using a parsimonious reconstruction together with effective distances, as defined by [ 8 ]. Effective distances are calculated based on passenger flows between airports.…”

“…Using these distances as a cost matrix, the parsimonious reconstruction identifies ancestral locations for internal nodes of the tree that minimize the distances along the phylogeny. This method allows rapid inferences of spread paths on a fine-grained geographical scale [ 82 ]. Reconstruction using effective distances infers phylogeographic spread more accurately than reconstruction using geographic distances or Bayesian reconstructions that do not use any distance information.…”

Computational Strategies to Combat COVID-19: Useful Tools to Accelerate SARS-CoV-2 and Coronavirus Research

“…using air travel or local diffusion, to reconstruct the putative spread paths and outbreak origins of rapidly evolving pathogens. Reimering et al [73] published a method that infers locations for internal nodes of a phylogenetic tree using a parsimonious reconstruction together with effective distances, as defined by Brockmann et al [74]. Effective distances are calculated based on passenger flows between airports.…”

“…Using these distances as a cost matrix, the parsimonious reconstruction identifies ancestral locations for internal nodes of the tree that minimize the distances along the phylogeny. This method allows rapid inferences of spread paths on a fine-grained geographical scale [73]. Reconstruction using effective distances infers phylogeographic spread more accurately than reconstruction using geographic distances or Bayesian reconstructions that do not use any distance information.…”

“…The code is included in the GitHub repository for Reimering et al [73] https://github.com/hzibifo/Phylogeography_Paper…”