Looking Glass: Protecting Canadians in a Return to Community

Analytics to help inform decision making around the health and economic impact of COVID-19 policies.

Project Budget * - $2.2M

Partner Co-investment - $0.9M

Supercluster Co-investment - $1.3M

Kings Distributed Systems – $1.30 M

*Contracted amounts as of September 30, 2020. 

Project Partners

Project Overview

Restrictions such as physical distancing, closures of some businesses and self-isolation have been key tools in flattening the COVID-19 curve. But these restrictions come with huge economic, social and mental costs for individuals, communities and businesses.

As schools, businesses and public spaces seek to reopen safely, there are no comprehensive sources for data-driven insight that municipalities, provincial health authorities or companies can use to forecast safe levels of interaction. While these sources exist in isolation, integration is missing, in short supply or contradictory – eroding the confidence of decision makers.

Moreover, current measures have no built-in feedback on their effectiveness. Given the unknowns about the virus, it’s tough to understand which measures will be most effective in flattening the COVID-19 curve.

The Looking Glass project team is building a decision-support platform that uses predictive modelling to analyze policies and determine which can best protect Canadians in the new normal.

Looking Glass is led by Kings Distributed Systems in partnership with Queen’s University, Riskthinking.AI, Limestone Analytics, aiSight, Distributed Compute Labs, CENGN, Server Cloud Canada, the Dymond Group, AMPD, Krate Distributed Information Systems, Saskatchewan Polytechnic, the University of Saskatchewan, as well as municipalities in Ontario, B.C., Newfoundland and Saskatchewan.

Decision makers and their team members will be able to engage with an interactive map of Canada on the Looking Glass website. The map will be broken down by census region and linked to demographic, economic and COVID-19 case report data and analysis that is unique to their region. In addition, anonymized data will visualize people’s movement between municipalities.

When fully developed, the platform could be adapted to test and inform the rollout of public health campaigns such as vaccinations, the management of tick-borne diseases, as well as nutrition, education and climate change initiatives.

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