Millions of gigabytes of data — the equivalent of a modest library — are being generated by the pandemic each day in medical records and other information on infected patients. Blood results. Age, race. Genetic testing. Interventions attempted. Outcomes. Now, nearly 10 months into the outbreak, scientists are starting to make connections in this jumble of letters and numbers with the help of artificial intelligence, leading to new theories about the virus and how to stop it.
While the human brain can process only so much information at a time, machines are whizzes at finding subtle patterns in huge amounts of data, and they are being deployed against covid-19 — the disease caused by the coronavirus — in ways only imagined in the past. Data scientists are aiming AI at some of the coronavirus’s biggest mysteries — why the disease looks so different in children vs. adults, what makes some people “superspreaders” while others don’t transmit the virus at all — and other, lesser questions we have made little headway in understanding.
Jason Moore, director of the Penn Institute for Biomedical Informatics at the University of Pennsylvania, who is helping put together an international covid-19 data consortium, said that if the virus had hit 20 years ago, the world might have been doomed.
“But I think we have a fighting chance today because of AI and machine learning,” he said.