Bayesian reconstruction of HIV transmission trees from viral sequences and uncertain infection times

TL;DR

This paper introduces a Bayesian method for accurately reconstructing HIV transmission trees using viral sequences and infection times, addressing limitations of previous approaches that relied solely on genetic data.

Contribution

The paper presents a novel Bayesian inference approach that combines genetic sequences and infection times to improve transmission tree reconstruction accuracy.

Findings

Bayesian method outperforms existing approaches in simulations.

Infection time accuracy critically affects transmission tree inference.

Application to real data reveals insights into epidemic dynamics.

Abstract

Genetic sequence data of pathogens are increasingly used to investigate transmission dynamics in both endemic diseases and disease outbreaks; such research can aid in development of appropriate interventions and in design of studies to evaluate them. Several methods have been proposed to infer transmission chains from sequence data; however, existing methods do not generally reliably reconstruct transmission trees because genetic sequence data or inferred phylogenetic trees from such data are insufficient for accurate inference regarding transmission chains. In this paper, we demonstrate the lack of a one-to-one relationship between phylogenies and transmission trees, and also show that information regarding infection times together with genetic sequences permit accurate reconstruction of transmission trees. We propose a Bayesian inference method for this purpose and demonstrate that precision of inference regarding these transmission trees depends on precision of the estimated times of infection. We also illustrate the use of these methods to study features of epidemic dynamics, such as the relationship between characteristics of nodes and average number of outbound edges or inbound edges-- signifying possible transmission events from and to nodes. We study the performance of the proposed method in simulation experiments and demonstrate its superiority in comparison to an alternative method. We apply them to a transmission cluster in San Diego and investigate the impact of biological, behavioral, and demographic factors.