Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics

Abstract

In the midst of an outbreak, identification of groups of individuals that represent risk for transmission of the pathogen under investigation is critical to public health efforts. Several approaches exist that utilize the evolutionary information from pathogen genomic data derived from infected individuals to distinguish these groups from the background population, comprised of primarily randomly sampled individuals with undetermined epidemiological linkage. These methods are, however, limited in their ability to characterize the dynamics of these groups, or clusters of transmission. Dynamic transmission patterns within these clusters, whether it be the result of changes at the level of the virus (e.g., infectivity) or host (e.g., vaccination implementation), are critical in strategizing public health interventions, particularly when resources are limited. Phylogenetic trees are widely used not only in the detection of transmission clusters, but the topological shape of the branches within can be useful sources of information regarding the dynamics of the represented population. We evaluate the limitation of existing tree shape statistics when dealing with smaller subtrees containing transmission clusters and offer instead a phylogeny based deep learning system (DeepDynaTree) for classification of transmission cluster. Comprehensive experiments carried out on a variety of simulated epidemic growth models indicate that this graph deep learning approach is effective in predicting cluster dynamics (balanced accuracy of 0.826 vs. 0.533 and Brier score of 0.234 vs. 0.466 in independent test set). Our deployment model in DeepDynaTree incorporates a primal-dual graph neural network principle using output from phylogenetic-based cluster identification tools (available from https://github.com/salemilab/DeepDynaTree).