Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.07.17.549268v1?rss=1
Authors: Sun, C., Fang, R., Salemi, M., Prosperi, M., Rife Magalis, B.
Abstract:
In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful to reconstruct the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylogenetic trees for infection forecasting in addition to backtracking, developing a phylogeny-based deep learning system, called DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, and it is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at: https://github.com/lab-smile/DeepDynaForcast.
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