  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.
