For healthcare stakeholders around the world, COVID-19 is many things, including “a big data problem.”

That’s how James Hendler, the Tetherless World Professor of Computer, Web, and Cognitive Science at Rensselaer Polytechnic Institute (RPI) and director of the Rensselaer Institute for Data Exploration and Applications (IDEA), recently summed it up in an interview with HealthITAnalytics.

What he means, of course, is that as the virus has proliferated so, too, have the questions about why it affects some people more than others, what measures can help reduce the spread and where the disease will likely go next.  And finding the answers to all of those questions and more begins with collecting and analyzing data from around the world.

For example, Hendler explained, one data component focuses on biomedical research.

“A lot of work is going on to try to develop a vaccine to find out whether there are any current drugs work against COVID-19,” he said. “All of those projects require molecular modeling, and many of them are using AI and machine learning to map things we know about the virus to things in pharmacological databases and genomic databases.”

Another component is relatively new to attempts to understand and address pandemics: tapping into social media in order to understand how the disease is impacting the public.  

“What can we learn about how people are bearing the burdens and stresses of the pandemic?” Hendler explained the intention. “With SARS and other outbreaks, we never really had to figure out how different social distancing techniques are impacting the spread in different places. You can't just compare numbers, because there are a lot of other factors to consider. AI is very good at that kind of multi-factor learning and a lot of people are trying to apply those techniques now.”

A third use for AI has already been used in healthcare circles, but it’s coming in particularly useful as researchers scramble to get a handle on the virus: mining scientific literature.

“In past years, you had hundreds of grad students reading papers and trying to figure out what was going on,” Hendler explained. “At many universities, there's a lot of effort to say, ‘What can we learn from what’s already been published?’”

As the article sums up the situation, the unprecedented impact of coronavirus around the world has sparked the need for unprecedented partnerships, and Hendler is keen on the ways AI can make a contribution.

“In healthcare, academia and industry are mostly set up for people to stay in their own lanes. But people are rapidly beginning to realize that attacking this problem is going to require a collaborative effort,” he said.

“To make any real progress in this situation, you need to bring together people who understand the computation and AI, people who understand the biological and biomedical implications, and people who understand population models. It's a very interdisciplinary problem, and to make any headway, we need the data and we need the team.”