  Reconstructing pathogen dynamics from genetic data as they become available
during an outbreak or epidemic represents an important statistical scenario in
which observations arrive sequentially in time and one is interested in
performing inference in an 'online' fashion. Widely-used Bayesian phylogenetic
inference packages are not set up for this purpose, generally requiring one to
recompute trees and evolutionary model parameters de novo when new data arrive.
To accommodate increasing data flow in a Bayesian phylogenetic framework, we
introduce a methodology to efficiently update the posterior distribution with
newly available genetic data. Our procedure is implemented in the BEAST 1.10
software package, and relies on a distance-based measure to insert new taxa
into the current estimate of the phylogeny and imputes plausible values for new
model parameters to accommodate growing dimensionality. This augmentation
creates informed starting values and re-uses optimally tuned transition kernels
for posterior exploration of growing data sets, reducing the time necessary to
converge to target posterior distributions. We apply our framework to data from
the recent West African Ebola virus epidemic and demonstrate a considerable
reduction in time required to obtain posterior estimates at different time
points of the outbreak. Beyond epidemic monitoring, this framework easily finds
other applications within the phylogenetics community, where changes in the
data -- in terms of alignment changes, sequence addition or removal -- present
common scenarios that can benefit from online inference.
