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Title: Inference of Transmission Network Structure from HIV Phylogenetic Trees

Abstract

Phylogenetic inference is an attractive means to reconstruct transmission histories and epidemics. However, there is not a perfect correspondence between transmission history and virus phylogeny. Both node height and topological differences may occur, depending on the interaction between within-host evolutionary dynamics and between-host transmission patterns. To investigate these interactions, we added a within-host evolutionary model in epidemiological simulations and examined if the resulting phylogeny could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivity across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic. Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phylogeny (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phylogeny reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures themore » transmission tree, the diversification process and infectivity dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3];  [4];  [2]
  1. Stockholm Univ. (Sweden). Dept. of Mathematics; Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. Karolinska Inst., Stockholm (Sweden). Dept. of Microbiology, Tumor and Cell Biology; Karolinska Univ. Hospital, Stockholm (Sweden)
  4. Stockholm Univ. (Sweden). Dept. of Mathematics
Publication Date:
Research Org.:
Stockholm Univ. (Sweden); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE; National Inst. of Health (NIH) (United States); Swedish Research Council (SRC)
Contributing Org.:
Karolinska Inst., Stockholm (Sweden); Karolinska Univ. Hospital, Stockholm (Sweden)
OSTI Identifier:
1360708
Report Number(s):
LA-UR-16-27500
Journal ID: ISSN 1553-7358
Grant/Contract Number:  
AC52-06NA25396; R01AI087520; 340-2013-5003
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 13; Journal Issue: 1; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; HIV-1; Cherries; Network analysis; Viral evolution; Infectious disease epidemiology; Phylogenetics; Phylogenetic analysis; Epidemiological statistics

Citation Formats

Giardina, Federica, Romero-Severson, Ethan Obie, Albert, Jan, Britton, Tom, and Leitner, Thomas. Inference of Transmission Network Structure from HIV Phylogenetic Trees. United States: N. p., 2017. Web. doi:10.1371/journal.pcbi.1005316.
Giardina, Federica, Romero-Severson, Ethan Obie, Albert, Jan, Britton, Tom, & Leitner, Thomas. Inference of Transmission Network Structure from HIV Phylogenetic Trees. United States. doi:10.1371/journal.pcbi.1005316.
Giardina, Federica, Romero-Severson, Ethan Obie, Albert, Jan, Britton, Tom, and Leitner, Thomas. Fri . "Inference of Transmission Network Structure from HIV Phylogenetic Trees". United States. doi:10.1371/journal.pcbi.1005316. https://www.osti.gov/servlets/purl/1360708.
@article{osti_1360708,
title = {Inference of Transmission Network Structure from HIV Phylogenetic Trees},
author = {Giardina, Federica and Romero-Severson, Ethan Obie and Albert, Jan and Britton, Tom and Leitner, Thomas},
abstractNote = {Phylogenetic inference is an attractive means to reconstruct transmission histories and epidemics. However, there is not a perfect correspondence between transmission history and virus phylogeny. Both node height and topological differences may occur, depending on the interaction between within-host evolutionary dynamics and between-host transmission patterns. To investigate these interactions, we added a within-host evolutionary model in epidemiological simulations and examined if the resulting phylogeny could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivity across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic. Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phylogeny (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phylogeny reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivity dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.},
doi = {10.1371/journal.pcbi.1005316},
journal = {PLoS Computational Biology (Online)},
number = 1,
volume = 13,
place = {United States},
year = {Fri Jan 13 00:00:00 EST 2017},
month = {Fri Jan 13 00:00:00 EST 2017}
}

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