Emergenet: A Digital Twin of Sequence Evolution for Scalable Emergence Risk Assessment of Animal Influenza A Strains
Kevin Yuanbo Wu, Jin Li, Aaron Esser-Kahn, Ishanu Chattopadhyay

TL;DR
Emergenet is a scalable digital twin tool that predicts the emergence potential of animal influenza strains, outperforming traditional methods and enabling rapid risk assessment for pandemic prevention.
Contribution
This paper introduces Emergenet, a novel digital twin framework for sequence evolution that improves emergence prediction accuracy and speed compared to existing approaches.
Findings
Emergenet outperforms WHO vaccine recommendations in H1N1/H3N2 prediction.
The model's emergence risk scores strongly correlate with CDC's IRAT scores.
Achieved over five orders of magnitude speedup in risk assessment, analyzing thousands of strains.
Abstract
Despite having triggered devastating pandemics in the past, our ability to quantitatively assess the emergence potential of individual strains of animal influenza viruses remains limited. This study introduces Emergenet, a tool to infer a digital twin of sequence evolution to chart how new variants might emerge in the wild. Our predictions based on Emergenets built only using 220,151 Hemagglutinnin (HA) sequences consistently outperform WHO seasonal vaccine recommendations for H1N1/H3N2 subtypes over two decades (average match-improvement: 3.73 AAs, 28.40\%), and are at par with state-of-the-art approaches that use more detailed phenotypic annotations. Finally, our generative models are used to scalably calculate the current odds of emergence of animal strains not yet in human circulation, which strongly correlates with CDC's expert-assessed Influenza Risk Assessment Tool (IRAT) scores…
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Taxonomy
TopicsAnimal Disease Management and Epidemiology · Influenza Virus Research Studies · Cell Image Analysis Techniques
