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Genetic sequence data of pathogens are increasingly used to investigatetransmission dynamics in both endemic diseases and disease outbreaks; suchresearch can aid in development of appropriate interventions and in design ofstudies to evaluate them. Several methods have been proposed to infertransmission chains from sequence data; however, existing methods do notgenerally reliably reconstruct transmission trees because genetic sequence dataor inferred phylogenetic trees from such data are insufficient for accurateinference regarding transmission chains. In this paper, we demonstrate the lackof a one-to-one relationship between phylogenies and transmission trees, andalso show that information regarding infection times together with geneticsequences permit accurate reconstruction of transmission trees. We propose aBayesian inference method for this purpose and demonstrate that precision ofinference regarding these transmission trees depends on precision of theestimated times of infection. We also illustrate the use of these methods tostudy features of epidemic dynamics, such as the relationship betweencharacteristics of nodes and average number of outbound edges or inboundedges-- signifying possible transmission events from and to nodes. We study theperformance of the proposed method in simulation experiments and demonstrateits superiority in comparison to an alternative method. We apply them to atransmission cluster in San Diego and investigate the impact of biological,behavioral, and demographic factors.

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