AI models are being used to track zoonotic diseases. Will they prevent the next pandemic?
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Many infectious diseases that affect humans are zoonotic — that is, they originate in other animals. This includes such scourges as SARS-CoV-2, the virus that caused the COVID-19 pandemic, and ebolaviruses, which are thought to have originated in fruit bats of the Pteropodidae family. Humans can catch ebolaviruses directly from bats’ bodily fluids, or by way of animals that have come into contact with infected bats.
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Holmes develops machine-learning models to identify new zoonotic viruses in samples that were not necessarily collected for that purpose. He uses sequencing data from public resources such as GenBank or Pathoplexus, an open-source database of viral pathogens. The models are trained on the sequences of all known human pathogens, so they can recognize traits associated with disease emergence in people. “The cell receptors, modes of transmission — all those sorts of things play into these AI algorithms,” Holmes explains. The models then learn to identify what Holmes terms “risky” viruses that researchers can look out for in the field, and even design prophylactic vaccines against them.
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Beyond virus identification, AI can also be used to forecast and monitor outbreaks. BlueDot, a firm in Toronto, Canada, that assesses infectious-disease risk, provides these services for clients such as the Gulf Center for Disease Prevention and Control in Riyadh and the City of Chicago, Illinois. The company uses AI to collect and filter thousands of articles and official data from public-health organizations, translating 65 languages to pull out information on specific diseases and symptoms. It then integrates further sources such as air-travel ticket sales to advise clients that “these are the risks that are most connected to your location, based on the way that people are moving”, explains Andrea Thomas, the company’s vice-president of epidemiology and data science.
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