Machine learning could help to identify the viruses most likely to spill over from animals to people and cause future pandemics. In February 2021, seven Russian poultry-farm workers were reported to have been infected with H5N8 avian influenza. This subtype of bird flu had never been known to infect people before, and the virus's genetic sequence was quickly uploaded to the genetic data repository GISAID. For Colin Carlson, a biologist at Georgetown University in Washington DC, it presented an opportunity. "I immediately thought, 'I want to run this through FluLeap'," he says. FluLeap is a machine-learning algorithm that uses sequence data to classify influenza viruses as either avian or human.

"It feels like you have a new set of eyes," said Barbara Han, a disease ecologist at the Cary Institute of Ecosystem Studies in Millbrook, N.Y., who collaborates with Dr. Carlson. "You just can't see in as many dimensions as the model can." Dr. Han first came across machine learning in 2010. Computer scientists had been developing the technique for decades, and were starting to build powerful tools with it. These days, machine learning enables computers to spot fraudulent credit charges and recognize people's faces.