Title:Artificial intelligence helps profile virus hosts

Where should we be looking for the next SARS? That’s the question being asked by Colin Carlson, PhD, an assistant research professor in the Depart- ment of Microbiology & Immunology and a member of the Center for Global Health Science and Security at Georgetown University Medical Center.

Carlson and his team recently published a study in Lancet Microbe demonstrating that artificial intelligence can help predict which viruses could infect humans— like SARS-CoV-2, the virus that led to the COVID-19 pandemic—as well as which animals host them, and where they could emerge.

“If you want to find these viruses, you have to start by profiling their hosts: their ecology, their evolution, even the shape of their wings,” explains Carlson. “Artificial intelligence lets us take data on bats and turn it into concrete predictions.”

In the first quarter of 2020, the Georgetown-based research team, in collaboration with 12 other universities and the American Museum of Natural History, developed eight different statistical models that predicted which kinds of animals could host betacoronaviruses. Over the course of 18 months, the team tracked the discovery of 40 new bat hosts of betacoronaviruses to validate initial predictions and dynamically update their models.

The researchers found that models harnessing data on bat ecology and evolution performed extremely well at predicting new hosts. In contrast, cutting-edge models from network science that used high-level mathematics—but less biological data—performed roughly as well or worse than expected at random.

“If we spend less money, resources, and time looking for these viruses, we can put all of those resources into the things that actually save lives down the road. We can invest in building universal vaccines to target those viruses, or monitoring for spillover in people that live near bats,” says Carlson. “It’s a win-win for science and public health.”