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DOE OSTI · 1844116

Using Deep Mutational Data and Machine Learning to Guide Outbreak and Pandemic Response

Hu, Bin·Gans, Jason David·Li, Po-E·Lin, Youzuo·Chain, Patrick Sam Guy
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Abstract

A significant fraction of pathogens known to infect humans originate in non-human (zoonotic) hosts (Taylor, Latham, and Woolhouse 2001), and new and emerging pathogens continue to spill over into the human population more frequently at an alarming rate (e.g., SARS, MERS, Cholera, etc.). The recent outbreaks of Ebola virus in West Africa and the ongoing SARS-CoV-2 pandemic demonstrate the need for rapid and reliable assessments of viral phenotype information to help inform scientists and policy makers how best to control the spread of disease. Further understanding of the virus pathogenic evolutionary space and potential trajectory could guide appropriate control measures to limit the spread of a new virus throughout the local and global human population.

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59 BASIC BIOLOGICAL SCIENCESbiological sciencesCOVID-19genomic mutationsmachine learningSARS-CoV-2

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Hu, Bin, Gans, Jason David, Li, Po-E, Lin, Youzuo, Chain, Patrick Sam Guy. 2022-02-07. Using Deep Mutational Data and Machine Learning to Guide Outbreak and Pandemic Response. https://doi.org/10.2172/1844116

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