Project Description: 

This project monitors fast-changing public support, awareness, and uptake with adaptive, periodic and restrictive public health measures that Canada and other countries may need to adopt due to the emerging reality of an endemic COVID-19 that cannot be eradicated. The changing extent to which the public adheres to, and is supportive of, effective, adaptive restrictive measures can enable health system resilience and minimize excess morbidity and mortality. Accurately measuring public enthusiasn and support for restrictive measures will enable public health agencies and vaccination clinics to identify location-specific, demographic-specific and culturally appropriate messaging/interventions to increase information equity for, and readiness among, at-risk communities and populations.

PROJECT GOALS: 

(a) Use mathematical models to calculate the relative readiness of different populations for adaptive restrictive policy initiatives of varying magnitude

(b) Create a barometer of dynamic readiness for restrictions across broad populations

(c) Build a predictive forecasting model to help inform the ways in which readiness drives the effectiveness of different restrictive public health policies

(d) Provide an authoritative public portal to enable knowledge translation of these models and the data for the public and key stakeholders

Through a multi-country survey in a range of  low, middle and high-income countries, with a concentration of the respondents from Canada, using a non-incentivized, random, and anonymous survey engagement model, we will compare regions and countries and access the broadest possible group of online respondents. This survey speificially targets individuals who rarely, if ever, respond to surveys. Through this project readiness measures will be designed and employed that are optimized both for user experience and enhanced, long-term modeling. Readiness items will include both public perception assessment and personal readiness assessment. Data analysis will employ a number of techniques including clustering analysis, triangulation and dimensionality reduction. 

The Research Team:  

Kumar Murty

Professor Murty received his doctorate from Harvard University in 1982. From 1982 to 1987, he held research positions at the Institute for Advanced Study at Princeton, Concordia University, and the Tata Institute of Fundamental Research. In 1987, Prof. Murty was appointed as Associate Professor at the Downtown campus of the University of Toronto, and in 1991 was promoted to Full Professor. Twice, Prof. Murty was Chair of the Department of Mathematics at the University of Toronto Downtown campus. With almost 40 years of experience in mathematical sciences at the local, national, and global level, Prof. Murty’s mathematical accomplishments cover diverse areas including analytic number theory, algebraic number theory, information security, arithmetic algebraic geometry, and mathematical modelling. He has served on the Canadian Mathematical Society Board of Directors and held vice presidency at the Canadian Mathematical Society. Prof. Murty was elected a Fellow of the Royal Society of Canada in 1995, Fields Institute Fellow in 2003, and Senior Fellow of Massey College in 2020. He received the Coxeter-James Prize in 1991 and the University of Toronto’s Inventor of the Year Award in 2011. Prof. Murty is the current director of the Fields Institute for Research in Mathematical Sciences. From March 2020 to September 2021, he co-chaired the Modelling Consensus Table in Ontario to provide real-time modelling on the pandemic. He has over 120 published articles in leading scholarly journals and extensive involvement with external committees. 

Neil Seeman 

Neil is an expert in global public health policy risk analysis, trend identification, and prediction. He is a Senior Fellow at the Institute for Health Policy, Management and Evaluation in the University of Toronto. He has taught courses on knowledge transfer and the Internet at the University of Toronto and Ryerson University. He is a Senior Academic Advisor to the Investigative Journalism Bureau at the Dalla Lana School of Public Health, where he supervises graduate students in diverse fields in the ethical use of survey and other data collection techniques to identify emergent public health trends. He is the recipient of many awards in data collection processes, including the Next Generation Market Research Award (Disruptive Innovation Award, 2014), and has been recognized for his novel data collection methods to improve the uptake of vaccines for COVID-19, with recognition from the 2021 Sternfels Prize for Drug Safety Discoveries (finalist), and in the University of Toronto’s Council of Health Sciences Springboard funding award (recipient, 2021). Neil holds a Juris Doctor from the University of Toronto’s Faculty of Law and a Master of Public Health degree from Harvard University. He is the co-author of four books on public health, hundreds of articles in international media, and more than 25 peer-reviewed papers. Neil is the inventor of random domain intercept technology, a patented approach to global survey data collection that has been applied by leading public health organizations around the world to measure health system resilience in low, middle- and high-income countries. 

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