Online Bayesian Phylodynamic Inference in BEAST with Application to Epidemic Reconstruction

Abstract

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 removalpresent common scenarios that can benefit from online inference.

Untangling introductions and persistence in COVID-19 resurgence in Europe

“…As we updated our dataset following initial analyses of the 2,909 genome collection using the approach discussed in the previous subsection, we sought to capitalize on these efforts to limit the burn-in for subsequent analyses of the 3,959 dataset. Specifically, we adopted the distance-based procedure to insert new taxa into a time-measured phylogenetic tree sample as implemented in the BEAST framework for online inference 36 . We subsequently use the augmented tree as the starting tree for the analyses of the updated dataset.…”

Phylogenetic and Drug-Resistance Analysis of HIV-1 Sequences From an Extensive Paediatric HIV-1 Outbreak in Larkana, Pakistan

“…The dates of origin (time to the most recent common ancestor; tMRCA) of the large Pakistani-specific HIV-1 clusters were estimated using Bayesian Markov Chain Monte Carlo (MCMC) inference in BEAST (v1.10.4) (Gill et al, 2020). All Larkana outbreak sequences were sampled in 2019 and did not independently have a sufficient temporal signal for inference of the dates of origin.…”

Exploiting genomic surveillance to map the spatio-temporal dispersal of SARS-CoV-2 spike mutations in Belgium across 2020

“…Instead, the entire pipeline would have to be run again when new data becomes available. In future work, we will evaluate the applicability and feasibility of novel approaches to accommodate phylogenetic uncertainty through parsimonious phylogenetic placement initiatives 27 , 28 , which may be able to provide a measure of uncertainty for newly sequenced genomes placed onto an existing phylogeny that was obtained in a previous analysis (i.e. an approach that has been shown to improve analysis time requirements 28 ).…”