The Rapid Response Program is designed to support CANSSI members who are in the position to provide critical research support on rapidly emerging problems important to society on a provincial, national, and/or international scale. The program is focused on applied research that has strong potential to have an immediate impact.
How to Apply to the Current Call for COVID-19 Projects
Do you have a applied research idea related to COVID-19 that will have an immediate impact with the current pandemic? Our Rapid Response Program can help you support graduate students and/or postdocs as well as the costs of data acquisition over a period of 2-4 months. Find out more about our call for proposals related to COVID-19.
Projects Currently Funded
Estimating the Number of Hidden COVID-19 Cases
Led by Laura Cowen, Junling Ma and Pauline van den Driessche, University of Victoria
Cases of COVID-19 have gone undetected due to factors such as volume of virus testing, asymptomatic patients, incorrect self-diagnosis, or failure to disclose to a health authority. Undetected cases may drive community infections and reduce the effectiveness of control measures. In order to investigate the rates of under-reporting of COVID-19 in Canada, CANSSI members will be adapting their methods from wildlife ecology to better quantify the real scope of this pandemic.
Currently, only people with symptoms are being tested for COVID-19, which means there are possibly hidden cases in Canada due to the current testing approach. By adapting hidden-population methods, which are used in studies that involve injection drug users and the homeless population, this team aims to uncover more data about hidden COVID-19 cases. This will enable health authorities to make more informed public health policies in the current and future waves of COVID-19. Understanding the scope of this disease is vital in assessing current health care and social responses. This work can also be used to better understand under-reporting of future pandemics.
Host Genetics for SARS-CoV-2 Severity and Infection: Power and Control
Led by Lloyd T. Elliot from Simon Fraser University
People who have contracted COVID-19 may have important information about their genetic makeup that gives researchers more insight into how the disease develops in an individual’s body. Lloyd’s team aims to better understand how people’s different genetic variations can inform COVID-19 vaccine and drug development. Specifically, Lloyd and his team will perform preliminary studies on genes involved in the infectious pathways of coronaviruses such as angiotensin-converting enzyme 2.
In addition, this team is using data from the UK Biobank to better understand and share how factors like ethnicity and socioeconomic status can impact research that investigates human genetics’ role in the body’s response to COVID-19. Because this pandemic is only just leaving its early stages, infections follows geographic and family lines. This means researchers need to control for socioeconomic structures to ensure that the solutions developed to address COVID-19 are accurate and consider a diverse population.
Improved Estimation of Social Contact Matrices for Modeling the COVID-19 Epidemic in Canada
Led by Marc Brisson and Mélanie Drolet who are both members of the population measures group of the Institut national de santé publique du Québec
Physical distancing is important in order to slow down and control the spread of COVID-19. Without a widely available vaccine, reducing physical contact is crucial, hence the importance of estimating the number of social contacts by population surveys. This project will aim to publish social contract matrices that will be shared with mathematical modellers and Canadian public health authorities to model the evolution of the COVID-19 pandemic.
The social contact matrices will use CONNECT survey data before and during confinement in Canada to establish expected contacts with re-openings currently underway across Canada. This data will then be integrated into this team’s dynamic mathematical model of transmission of COVID-19, developed specifically to guide decision-making in public health in Quebec. This project will enable health authorities to make more data-informed decision in their physical distancing measures and strategies as well as the gradual lifting of these measures.
Statistical Methods for COVID-19 Mortality Forecasting at the Small-Area Level
Led by Jeffrey Rosenthal, University of Toronto
The Centre for Global Health Research have recently developed the Global COVID-19 Assessment of Mortality (GCAM) tool, which is an open, transparent, and continuously updated statistical model that combines actual COVID-19 mortality counts with Bayesian inference to forecast COVID-19 deaths.
This project will improve the current GCAM tool by creating spatio-temporal predictions of COVID-19 mortality at the small area level with a hierarchical model; and accounting for irregularities in reporting times of non-hospital mortality by treating true event dates as latent variables. By improving this tool, this will allow the team to make the best possible use of COVID-19 mortality data as the pandemic spreads through its first wave, aiding countries in understanding national disease trajectories, and setting the stage for anticipating the course of future waves.