tetano
Editor, Senior Moderator
PLoS Comput Biol. 2020 Feb 7;16(2):e1007101. doi: 10.1371/journal.pcbi.1007101. [Epub ahead of print] [h=1]Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic.[/h]
Reimering S[SUP]1[/SUP], Mu?oz S[SUP]1[/SUP], McHardy AC[SUP]1,[/SUP][SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
PMID: 32032362 DOI: 10.1371/journal.pcbi.1007101
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Reimering S[SUP]1[/SUP], Mu?oz S[SUP]1[/SUP], McHardy AC[SUP]1,[/SUP][SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
PMID: 32032362 DOI: 10.1371/journal.pcbi.1007101
Free full text