Two weeks ago, as Shrey Jain (Year 1 EngSci) and his classmates received notice that all their courses were moving online, they saw an opportunity to help flatten the curve.
“I’ve always been interested in machine learning, and developing for social impact,” he says. “With everyone leaving campus and heading back home, I thought it would be a good idea to collaborate on an online project that would use the skills we have to help people.”
Jain is the leader of FLATTEN.ca, a volunteer-driven push to leverage big data in the fight against COVID-19. In just over a week, he and more than 25 other collaborators created an online platform that provides a real-time heatmap of potential and confirmed COVID-19 cases.
“There are other mapping tools out there that can tell you about cases in Canada or even Toronto,” says Jain. “But you don’t get to see what’s going on in your neighbourhood. We wanted to focus on more granular data.”
FLATTEN works by enabling users to anonymously self-report potential cases of COVID-19, based on a series of screening questions that the team developed in consultation with public health professionals.
The heatmap is localized using the first three digits of the user’s postal code. The hope is that it will enable governments to quickly identify areas that require the most attention.
The reliance on self-reporting does raise the risk that the data could be biased. False positives could result from people misreading their own symptoms, or bad actors who submit false information on purpose. But Jain says the same issues are present in other tools, such as hotlines set up by local health authorities.
“When you call, you are presented with screening questions very similar to the ones we use, so those results can be biased in exactly the same ways,” says Jain. “Of course, we are working on preventing abuse by tracking things like duplicate submissions or suspicious IP addresses. But at the end of the day, we think our system can quickly provide information that is valuable to Canadian healthcare systems.”
In addition to self-reported potential cases, the map contains two other layers. The first tracks the density of people in vulnerable demographics, such as elderly individuals with underlying health conditions or those who are immunosuppressed.
The other shows confirmed cases, drawing on publicly available data, similar to other tools developed by U of T teams.
The FLATTEN team is multidisciplinary, composed of engineering, computer science and molecular genetics students from U of T, the University of Waterloo, the University of New Brunswick and McMaster University. The team also lists several U of T professors among its advisors, in fields from public health to computer science.
“This platform would not be possible without the volunteered time of all our team and especially our team leaders Martin Staadacker, Yifei Zhang and Arthur Allshire,” says Jain. “In a very short period of time, this group went from a team of students working on project to a non-profit organization. If it helps flatten the curve, it’s all worth it.”
Right now, the team is focusing on the GTA as a pilot project. Key to its success will be widespread adoption — the more data the system collects, the more useful it will become.
“Based on our conversations with experts, we think that if we can get data from 10 per cent of the GTA population, which would be around 600,000 responses, the resulting map would be very valuable,” says Jain. “We want to get the word out to as many citizens as we possibly can.”