Inferring epidemic contact structure from phylogenetic trees.
Contact structure is believed to have a large impact on epidemic spreading and consequently using networks to model such contact structure continues to gain interest in epidemiology. However, detailed knowledge of the exact contact structure underlying real epidemics is limited. Here we address the...
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2012-01-01
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doaj-e7921c13289d44fca8d68e80df4d24212021-04-21T14:55:15ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582012-01-0183e100241310.1371/journal.pcbi.1002413Inferring epidemic contact structure from phylogenetic trees.Gabriel E LeventhalRoger KouyosTanja StadlerViktor von WylSabine YerlyJürg BöniCristina CelleraiThomas KlimkaitHuldrych F GünthardSebastian BonhoefferContact structure is believed to have a large impact on epidemic spreading and consequently using networks to model such contact structure continues to gain interest in epidemiology. However, detailed knowledge of the exact contact structure underlying real epidemics is limited. Here we address the question whether the structure of the contact network leaves a detectable genetic fingerprint in the pathogen population. To this end we compare phylogenies generated by disease outbreaks in simulated populations with different types of contact networks. We find that the shape of these phylogenies strongly depends on contact structure. In particular, measures of tree imbalance allow us to quantify to what extent the contact structure underlying an epidemic deviates from a null model contact network and illustrate this in the case of random mixing. Using a phylogeny from the Swiss HIV epidemic, we show that this epidemic has a significantly more unbalanced tree than would be expected from random mixing.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22412361/?tool=EBI |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Gabriel E Leventhal Roger Kouyos Tanja Stadler Viktor von Wyl Sabine Yerly Jürg Böni Cristina Cellerai Thomas Klimkait Huldrych F Günthard Sebastian Bonhoeffer |
spellingShingle |
Gabriel E Leventhal Roger Kouyos Tanja Stadler Viktor von Wyl Sabine Yerly Jürg Böni Cristina Cellerai Thomas Klimkait Huldrych F Günthard Sebastian Bonhoeffer Inferring epidemic contact structure from phylogenetic trees. PLoS Computational Biology |
author_facet |
Gabriel E Leventhal Roger Kouyos Tanja Stadler Viktor von Wyl Sabine Yerly Jürg Böni Cristina Cellerai Thomas Klimkait Huldrych F Günthard Sebastian Bonhoeffer |
author_sort |
Gabriel E Leventhal |
title |
Inferring epidemic contact structure from phylogenetic trees. |
title_short |
Inferring epidemic contact structure from phylogenetic trees. |
title_full |
Inferring epidemic contact structure from phylogenetic trees. |
title_fullStr |
Inferring epidemic contact structure from phylogenetic trees. |
title_full_unstemmed |
Inferring epidemic contact structure from phylogenetic trees. |
title_sort |
inferring epidemic contact structure from phylogenetic trees. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Computational Biology |
issn |
1553-734X 1553-7358 |
publishDate |
2012-01-01 |
description |
Contact structure is believed to have a large impact on epidemic spreading and consequently using networks to model such contact structure continues to gain interest in epidemiology. However, detailed knowledge of the exact contact structure underlying real epidemics is limited. Here we address the question whether the structure of the contact network leaves a detectable genetic fingerprint in the pathogen population. To this end we compare phylogenies generated by disease outbreaks in simulated populations with different types of contact networks. We find that the shape of these phylogenies strongly depends on contact structure. In particular, measures of tree imbalance allow us to quantify to what extent the contact structure underlying an epidemic deviates from a null model contact network and illustrate this in the case of random mixing. Using a phylogeny from the Swiss HIV epidemic, we show that this epidemic has a significantly more unbalanced tree than would be expected from random mixing. |
url |
https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22412361/?tool=EBI |
work_keys_str_mv |
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