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, the current state-of-the-art. Our method extends the state-of-the-art 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.Author summaryInfluenza A viruses infect up to 5 million people in recurring epidemics every year. Further, viruses of zoonotic origin constantly pose a pandemic risk. Understanding the geographical spread of these viruses, including the origin and the main spread routes between cities, states or countries, could help to monitor or contain novel outbreaks. Based on genetic sequences and sampling locations, the geographic spread can be reconstructed along a phylogenetic tree. Our approach uses a parsimonious reconstruction with air transportation data and was verified using a simulation of the 2009 H1N1 influenza A pandemic. Applied to real sequence data of the outbreak, our analysis gave detailed insights into spread patterns of influenza A viruses, highlighting the origin as well as airports mainly involved in the spread.

Characterization of SARS-CoV-2 genetic structure and infection clusters in a large German city based on integrated genomic surveillance, outbreak analysis, and contact tracing

“…These findings likely reflect the increased genetic differentiation of SARS-CoV-2 genomes due to the ongoing accumulation of mutations as well as reduced international travel due to travel restrictions and lockdown measures. The resolution and accuracy of phylodynamic and phylogeographic analyses [Kühnert et al 2016, Lemey et al 2020, Reimering et al 2020] will likely benefit from this increased geographical localization of individual SARS-CoV-2 viral variants. Selective sweeps of viral types with increased transmissibility [Rambaut et al 2020], however, may counteract the observed trend of increasing geographical localization by reducing global SARS-CoV-2 genetic diversity.…”

Haploflow: Strain-resolvedde novoassembly of viral genomes

“…It is also key for genomic epidemiology, i.e. tracing viral spread using genomic information 71,72 and genome-based viral phenotyping 73 . Strains can differ in their phenotypes, such as virulence, resistance, or the degree of their immune resistance to host immunity, which may be critical for the choice of therapy.…”