The spread of COVID-19 has been slowed by physical distancing, self-isolation, lockdowns, masks and travel restrictions. Doing more testing and contact tracing of known cases has helped contain the spread. However, these approaches are ‘behind the virus’ – by at least 5-10 days. We need a way of getting ahead of the virus, before it spreads further. We know that about half the infections are transmitted by people who do not have any symptoms, and this is sufficient to cause a growing epidemic. Therefore, we need to find, isolate and test at least some asymptomatic people. Like intelligence officers hunting hidden terrorists, we need to prevent attacks before they happen, instead of chasing the culprits after the fact. In other words, we need to identify carriers before outbreaks occur, not after. Our team proposes a new way to do this.

The overall goal of this project is to develop models and an intervention prototype to predict which individuals are most likely to be exposed to COVID-19 and are therefore most at risk of onward transmission. We will apply statistical modeling and Artificial Intelligence (AI) methods to demographic, occupational, social networking and geolocation data. In simulations, we will test different ‘smart isolation and testing strategies’ that could be used by public health officials to determine which would most effectively reduce viral transmission. Applying AI-informed strategies could enable partial relaxation of confinement rules for lower-risk segments, and allow greater reopening of economic and social life without risking a second wave and further lockdowns.

Industry Partner(s):Larus Technologies

Academic Institution:University of Ottawa

Academic Researcher: Lise Bjerre

Focus Areas: COVID-19

Platforms: Cloud, GPU