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 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.
authors.affiliation: Department of Mathematics, Stockholm University, Stockholm, Sweden.|Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
authors.forename: Federica
authors.identifier: 0000-0002-3710-0940
authors.initials: F
authors.lastname: Giardina
authors.affiliation: Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
authors.forename: Ethan Obie
authors.identifier: 0000-0002-8082-1225
authors.initials: EO
authors.lastname: Romero-Severson
authors.affiliation: Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden.|Department of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden.
authors.forename: Jan
authors.identifier: 0000-0001-9020-0521
authors.initials: J
authors.lastname: Albert
authors.affiliation: Department of Mathematics, Stockholm University, Stockholm, Sweden.
authors.forename: Tom
authors.initials: T
authors.lastname: Britton
authors.affiliation: Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
authors.forename: Thomas
authors.initials: T
authors.lastname: Leitner
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology; D003198:Computer Simulation; D004196:Disease Outbreaks; D015658:HIV Infections / Q000635:transmission* / Q000821:virology*; D015497:HIV-1 / Q000145:classification* / Q000235:genetics*; D006801:Humans; D008954:Models, Biological; D010802:Phylogeny; D013548:Sweden