Inferring Viral Transmission Time from Phylogenies for Known Transmission Pairs.
Mol Biol Evol
; 41(1)2024 Jan 03.
Article
in En
| MEDLINE
| ID: mdl-38149995
ABSTRACT
When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously described methods:
a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the source's infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model-which make use of different information within a tree-suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
HIV Infections
Limits:
Humans
Language:
En
Journal:
Mol Biol Evol
Journal subject:
BIOLOGIA MOLECULAR
Year:
2024
Type:
Article
Affiliation country:
United States