Jesse Shapiro, McGill University, Pillar 6 Lead & Project Lead
Fiona Brinkman, Simon Fraser University, Pillar 6 Deputy
Caroline Colijn, Simon Fraser University, Pillar 6 Deputy
Morgan Langille, Dalhousie University, Pillar 6 Deputy
Carmen Lia Murall, Pillar 6 Research Associate
Sally Otto, University of British Columbia, Pillar 6 Deputy
Jen Gommerman, University of Toronto, Pillar 1 Lead
Ciro Piccirillo, McGill University, Pillar 1 Deputy
Louis Flamand, Université Laval, Pillar 3 Lead
Anne-Claude Gingras, Sinai Health Systems, Pillar 4 Lead
Ioannis Ragoussis, McGill University, Pillar 5 Lead
Nazeem Muhajarine, University of Saskatchewan, Pillar 8 Lead
Douglas Manuel, Ottawa Hospital Research Institute, Pillar 8 Deputy
Kimberly Huyser, University of British Columbia, Pillar 7 Lead
Dominic Frigon, McGill University
Gary Van Domselaar, Public Health Agency of Canada
Nick Ogden, Public Health Agency of Canada
Lindsay Whitmore, Public Health Agency of Canada
Sarah Dorner, Polytechnique Montréal
Computer modelling and data analysis have been key to the pandemic response and will continue to play a major role in the pandemic endgame: modelling COVID-19 case counts and hospitalizations, among other data, allows public health authorities and hospitals to consider likely scenarios and plan accordingly. The rapid analysis of virus genome sequencing data – both from clinical samples and wastewater – is also essential now for identifying mutations in the viral genome and tracking the rise of variant lineages.
Over the year to come in Canada, as people get fully vaccinated and cases (hopefully) dwindle, the pandemic endgame will shift to three major fronts: 1) surveillance of variants internationally to assess risks before they get to Canada; 2) analysis of wastewater sequencing data to pinpoint outbreaks before they spread; and 3) understanding which mutations are likely to reduce immunity and vaccine effectiveness. On all these fronts, sequencing alone is not enough. Viral genome sequences, along with key data such as time and place of sequencing, whether from a vaccinated individual, etc., must be shared in databases, analyzed using bioinformatics to put the data into context. The information must then be integrated with models of viral transmission and immune escape.
This project, led by CovaRR-Net’s Pillar 6: Computational Analysis, Modelling and Evolutionary Outcomes (CAMEO), involves:
- Creating a unified framework for genome sequencing data analysis across Canada, while providing support for other countries to share and analyze their own data.
- Developing methods to track mutations and variants via wastewater and ensuring that data is synthesized and integrated within the Public Health Agency of Canada (PHAC).
- Identifying mutations that occur in gene encoding epitopes (the part of the virus targeted by the immune system) and track their frequencies over time and space (nationally and internationally).
Together, these activities will support pandemic management strategies that are as proactive (or as rapidly reactive) as possible.
CoVaRR-Net: $125,000 cash contribution