In collaboration with Associate Professor Lise Bjerre of the University of Ottawa, Larus Technologies has proposed a novel, proactive approach known as predictive case identification to address the problem of early detection of asymptomatic/pre-symptomatic carriers of COVID-19.
By using a wide spectrum approach to data analysis at the individual-level, including AI/ML approaches for prediction, simulation and optimization, the team is aiming to create, simulate and evaluate a ‘smart isolation and testing strategy’ that would inform policy decision-making and allow partial reopening of economic and social life while minimizing the risk of a ‘second wave’ and further lockdowns. Harnessing data towards identifying people at-risk of contracting COVID-19 before they can spread the virus by predicting who is most likely to be infected is key to immediate isolation and targeted testing of pre-symptomatic and/or asymptomatic carriers. This could mean the difference between a prolonged lockdown, a second-wave — or the re-opening of ‘normal’ life.
Read the full announcement at https://www.soscip.org/covid19