Bayesian reconstruction of HIV transmission trees from viral sequences and uncertain infection times
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian InferenceSimilar Papers 제목 키워드 기반
Viral Proteins Reveal Geometry of Protein Language Models
Protein language models are trained on highly imbalanced datasets, raising the question of how they represent underrepresented biological sequences. Using viral proteins as a case study across ESM model families, we iden…
Protein Language ModelScalable Bayesian divergence time estimation with ratio transformations
Divergence time estimation is crucial to provide temporal signals for dating biologically important events, from species divergence to viral transmissions in space and time. With the advent of high-throughput sequencing,…
Modeling Homophily in Dynamic Networks with Application to HIV Molecular Surveillance
This paper describes a novel approach to modeling homphily, i.e. the tendency of nodes that share (or differ in) certain attributes to be linked; we consider dynamic networks in which nodes can be added over time but not…
Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention
Current methods for viral discovery target evolutionarily conserved proteins that accurately identify virus families but remain unable to distinguish the zoonotic potential of newly discovered viruses. Here, we apply an …
Maximum Likelihood de novo reconstruction of viral populations using paired end sequencing data
We present MLEHaplo, a maximum likelihood de novo assembly algorithm for reconstructing viral haplotypes in a virus population from paired-end next generation sequencing (NGS) data. Using the pairing information of reads…