When one thinks about computer science’s role in tackling COVID-19, it’s hard to imagine what it might be. Yet James Hughes is working on that very thing.
The assistant professor at St. Francis Xavier University has been spending the summer of 2020 trying to figure out how authorities can most effectively deliver what is bound to start as a finite number of vaccines to have the best chance of slowing the virus.
“Vaxing a population is perhaps more a programming problem than people might think,” Hughes says. “When the vaccines become available, we won’t have enough for everyone.”
The World Health Organization and the Centers for Disease Control have guidelines on who to vaccinate, which they establish after considering a number of factors, including risk economics and ethics. But, Hughes is working on a more dispassionate model that represents a community as a network of connected people.
“Imagine a remarkably simple network with three people,” he explains. “The middle person is connected to the other two, but the people at each end aren’t connected to each other. So if a person on the end gets COVID-19, we vaccinate the middle person, thereby saving them and the person at the other end. If that person is connected to a million other people, we just protected a million people with one vaccine.”
Lots of great minds have ideas on how to curb COVID once the vaccine is available but working out the math on them will become extremely labour intensive very quickly, he says. That’s where computers come in.
“We’re using artificial intelligence and machine learning to find strategies,” he says. “We’re doing simulations and the AI is generating a system of programs that tell us who to vaccinate.”
“The AI isn’t biased by pre-conceived notions,” he says. After they have the strategies, humans can then apply their ethical standards and tweak the answers if necessary.
He’s working with other teams — one in Guelph and one at Brock University — and is hoping to get a larger research community involved.
Hughes pivoted to COVID when it hit, but his research has always used machine learning to solve real-world problems for which answers aren’t otherwise readily available. The problems can come from kinematics, geology, music, finance and the human brain. He was recently, for example, doing some modelling on people with Parkinson’s, while they were using treadmills.
“We were using Compute Canada resources to come up with mathematical functions on how people walk,” he says. “I was also working towards coming up with a predictive model that will tell you what a traumatic brain-injury patient’s intercranial pressure is. The best way to test that is a dangerous neural surgery. We’re working to see if we could find another way that’s safer.”
His work also always requires intensive computer resources. In 2018 alone, he used “hundreds and hundreds” of core years. If he’d done the same work on a typical desktop computer, he would have had to hit “run” and return in 200 years for any findings, he jokes.