Rasha Kashef

Professors Dafna Sussman & Rasha Kashef of Ryerson University are teaming up with Mount Sinai Hospital to tackle the very difficult problem of achieving successful early intervention for pregnant women diagnosed with COVID-19.

The project aims to educate medical professionals directly treating COVID-19 pregnancies using a comprehensive, anonymized data repository in conjunction with dedicated prediction algorithms that will score patients’ risk for severe deterioration. The repository together with the algorithm are expected to radically transform Canadian and, potentially, International healthcare providers’ ability to identify, manage and treat cases of COVID-19 in pregnant patients.

Dafna Sussman
Steven Wang

Building on the success of Studio 1 Labs’ Intelligent Bed Sheet, York University’s Prof. Steven Wang and the team at Studio 1 Labs are looking to create an automated online monitoring system for abnormal respiratory patterns.

Accompanied by an AI-based severity index that scores breathing patterns to determine those at risk for faster health deterioration, they will provide a non-invasive solution to patient monitoring that also keeps a physical distance between patients and healthcare staff, in order to reduce the potential for transmission of the virus. Patients will simply only need to lay down on the Intelligent Bed Sheet for it to capture subtle symptoms that are not obvious to the naked eye, and can even be unknown to the patient themselves, radically changing the way that illness detection and monitoring can take place, at home and in medical settings.

Ed Shim

Ryerson University’s Xiao-Ping Zhang in concert with Altum View Systems Inc., are addressing the pressing need for remote monitoring of long-term care homes to ensure potential cases of COVID-19 are identified early, isolated and treated.

The collaborators will develop AI-based algorithms and systems to screen and monitor acoustic respiration patterns for COVID-19 in real-time using customer mobile devices (e.g., mobile phone, wireless headphone, smart wrist-watch etc.,) low cost electronic stethoscopes, and professional respiration monitor diagnostic devices. The COVID-19 acoustic respiration pattern screening and monitoring system will be incorporated into and complement Altum View Systems Inc.’s current vision-based health monitoring system for homecare and long term care facilities.

Steven Zhang

This deeply ambitious project, led by Professor Graham Taylor from the University of Guelph, in collaboration with Deep Biologics Inc., aims to combat SARS-CoV-2 at its source.

It will look to discover a means by which to neutralize and compete with the virus’ spike (S1 and S2) structural proteins, the primary docking mechanisms enabling the virus to infect human beings.  If successful, this research has the potential to prevent SARS-CoV-2 from attaching, fusing, and entering into the ACE2 enzyme of the human body, effectively acting as an anti-viral medicine that blocks the virus from infecting their target cells.

Graham Taylor
Xu (Sunny) Wang

This research project featuring a collaboration between Wilfrid Laurier’s Professor Xu Wang and Lovell Corporation will develop a self-adaptive recommendation engine to match skilled workers with proper voluntary actions in direct response to the increased community need created by the COVID-19 pandemic.

Additionally, this project will develop a voluntary measurement index to quantify the impact of voluntary actions against Canadian economic goals and targets after the COVID-19 pandemic to monitor and evaluate the reopening of the economy in Canada  The proposed research also provides opportunities to capture quality data to perform needs and gaps analysis for stakeholders to monitor progress, reassign volunteer and focus resources, and to equip Canadian businesses and governments to achieve operational efficiency gains and enhance resource mobilization.

Kelly Lovell
Lise Bjerre

This collaboration between Associate Professor Lise Bjerre of the University of Ottawa and Larus Technologies proposes 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 presymptomatic and/or asymptomatic carriers. This could mean the difference between a prolonged lockdown, a second-wave — or the re-opening of ‘normal’ life.

Rami Abielmona

This forward-thinking collaboration between the University of Ottawa’s Prof. Yongyi Mao and Advanced Symbolics Inc. aims to use AI to generate predictive models for forecasting and projecting the spread of COVID-19 cases across Canada.

By analyzing anonymized, user-generated data, the researchers expect to be able to determine accurate and timely models of viral spread across the country. Realistically capable of tracking the spread of any infectious disease, this project sets out an ambitious agenda to integrate a wide spectrum of data sources and data modalities as well as a diverse set of modern AI technologies, to fundamentally change the way we track, trace, and cope with epidemics and pandemics.

Yongi Mao

Professor Ghafar-Zadeh of York University and CMC Microsystems are researching the possibility of identifying COVID-19 in asymptomatic individuals using a ‘simple’ saliva test.

By employing the latest in Data Science and AI research on SOSCIP’s advanced computing platforms, the team hopes to develop a non-invasive, easy-to-use test that can be taken at home by anyone. Using advanced analytics on saliva samples in healthy and COVID-19-affected people, this project could have major impacts on national and international efforts to identify the spread of COVID-19 as and before it happens.

Ebrahim Ghafar-Zadeh