Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic.
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 algorith...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-02-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1007101 |
| _version_ | 1819260301620543488 |
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| author | Susanne Reimering Sebastian Muñoz Alice C McHardy |
| author_facet | Susanne Reimering Sebastian Muñoz Alice C McHardy |
| author_sort | Susanne Reimering |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T19:23:44Z |
| format | Article |
| id | doaj.art-3d0e4c149c0a4852a73c83082a7d4f8c |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-23T19:23:44Z |
| publishDate | 2020-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-3d0e4c149c0a4852a73c83082a7d4f8c2022-12-21T17:34:05ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-02-01162e100710110.1371/journal.pcbi.1007101Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic.Susanne ReimeringSebastian MuñozAlice C McHardyInfluenza 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.https://doi.org/10.1371/journal.pcbi.1007101 |
| spellingShingle | Susanne Reimering Sebastian Muñoz Alice C McHardy Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic. PLoS Computational Biology |
| title | Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic. |
| title_full | Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic. |
| title_fullStr | Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic. |
| title_full_unstemmed | Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic. |
| title_short | Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic. |
| title_sort | phylogeographic reconstruction using air transportation data and its application to the 2009 h1n1 influenza a pandemic |
| url | https://doi.org/10.1371/journal.pcbi.1007101 |
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