Mathematical modelling for pandemic preparedness in Canada: Learning from COVID-19.
Nicholas H Ogden, Emily S Acheson, Kevin Brown, David Champredon, Caroline Colijn, Alan Diener, Jonathan Dushoff, David Jd Earn, Vanessa Gabriele-Rivet, Marcellin Gangbè, Steve Guillouzic, Deirdre Hennessy, Valerie Hongoh, Amy Hurford, Lisa Kanary, Michael Li, Victoria Ng, Sarah P Otto, Irena Papst, Erin E Rees, Ashleigh Tuite, Matthew R MacLeod, Carmen Lia Murall, Lisa Waddell, Rania Wasfi, Michael Wolfson
Author Information
Nicholas H Ogden: Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada.
Emily S Acheson: Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada.
Kevin Brown: Public Health Ontario, Toronto, ON.
David Champredon: Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada.
Caroline Colijn: Department of Mathematics, Simon Fraser University, Burnaby, BC.
Alan Diener: Health Policy Branch, Health Canada, Ottawa, ON.
Jonathan Dushoff: Department of Biology and Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON.
David Jd Earn: Department of Mathematics and Statistics and Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON.
Vanessa Gabriele-Rivet: Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada.
Marcellin Gangbè: Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada.
Steve Guillouzic: Centre for Operational Research and Analysis, Defence Research and Development Canada, Department of National Defence, Ottawa, ON.
Deirdre Hennessy: Health Analysis Division, Analytical Studies and Modelling Branch, Statistics Canada, Ottawa, ON.
Valerie Hongoh: Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada.
Amy Hurford: Department of Biology and Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John's, NL.
Lisa Kanary: Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada.
Michael Li: Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada.
Victoria Ng: Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada.
Sarah P Otto: Department of Zoology and Biodiversity Research Centre, University of British Columbia, Vancouver, BC.
Irena Papst: Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada.
Erin E Rees: Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada.
Ashleigh Tuite: Dalla Lana School of Public Health, University of Toronto, Toronto, ON.
Matthew R MacLeod: Centre for Operational Research and Analysis, Defence Research and Development Canada, Department of National Defence, Ottawa, ON.
Carmen Lia Murall: Public Health Genomics Division, National Microbiology Laboratory, Public Health Agency of Canada.
Lisa Waddell: Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada.
Rania Wasfi: Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada.
Michael Wolfson: Faculty of Medicine and Faculty of Law-Common Law, University of Ottawa, Ottawa, ON.
Background: The COVID-19 pandemic underlined the need for pandemic planning but also brought into focus the use of mathematical modelling to support public health decisions. The types of models needed (compartment, agent-based, importation) are described. Best practices regarding biological realism (including the need for multidisciplinary expert advisors to modellers), model complexity, consideration of uncertainty and communications to decision-makers and the public are outlined. Methods: A narrative review was developed from the experiences of COVID-19 by members of the Public Health Agency of Canada External Modelling Network for Infectious Diseases (PHAC EMN-ID), a national community of practice on mathematical modelling of infectious diseases for public health. Results: Modelling can best support pandemic preparedness in two ways: 1) by modelling to support decisions on resource needs for likely future pandemics by estimating numbers of infections, hospitalized cases and cases needing intensive care, associated with epidemics of "hypothetical-yet-plausible" pandemic pathogens in Canada; and 2) by having ready-to-go modelling methods that can be readily adapted to the features of an emerging pandemic pathogen and used for long-range forecasting of the epidemic in Canada, as well as to explore scenarios to support public health decisions on the use of interventions. Conclusion: There is a need for modelling expertise within public health organizations in Canada, linked to modellers in academia in a community of practice, within which relationships built outside of times of crisis can be applied to enhance modelling during public health emergencies. Key challenges to modelling for pandemic preparedness include the availability of linked public health, hospital and genomic data in Canada.
