Robust phylodynamic analysis of genetic sequencing data from structured populations

Abstract

The multi-type birth-death model with sampling is a phylodynamic model which enables quantification of past population dynamics in structured populations, based on phylogenetic trees. The BEAST 2 package bdmm implements an algorithm for numerically computing the probability density of a phylogenetic tree given the population dynamic parameters under this model. In the initial release of bdmm, analyses were limited computationally to trees consisting of up to approximately 250 genetic samples. We implemented important algorithmic changes to bdmm which dramatically increase the number of genetic samples that can be analyzed, and improve the numerical robustness and efficiency of the calculations. Including more samples leads to improved precision of parameter estimates, particularly for structured models with a high number of inferred parameters. Furthermore, we report on several model extensions to bdmm, inspired by properties common to empirical datasets. We apply this improved algorithm to two partly overlapping datasets of Influenza A virus HA sequences sampled around the world, one with 500 samples, the other with only 175, for comparison. We report and compare the global migration patterns and seasonal dynamics inferred from each dataset. In that way, we show what information is gained by analyzing the bigger dataset which became possible with the presented algorithmic changes to bdmm. In summary, bdmm allows for robust, faster and more general phylodynamic inference of larger datasets.

Application of Phylodynamic Tools to Inform the Public Health Response to COVID-19: Qualitative Analysis of Expert Opinions (Preprint)

“…Since the beginning of the pandemic, the Nextstrain tool has traced SARS-CoV-2 by adding sequences in real time to a global epidemic tree, aiding with the localization of new infections within existing clusters. For example, heavily subsampled, custom Nextstrain analyses have been used to investigate the possible patient 0 in Italy [15] and undetected transmission at the beginning of the pandemic in the United States [16]. Aside from COVID-19, the platform has also been used to forecast influenza A strains for vaccine predictions [17] and to create situation reports for the Ebola outbreak in the Democratic Republic of the Congo in 2018 [18].…”