Canada's Provincial Covid-19 Pandemic Modelling Efforts: A Review of Mathematical Models and Their Impacts on the Responses

32 Pages Posted: 22 Jun 2023

See all articles by Yiqing Xia

Yiqing Xia

McGill University - Epidemiology, Biostatistics, & Occupational Health

Jorge Luis Flores Anato

McGill University - Epidemiology, Biostatistics, & Occupational Health

Caroline Colijin

Simon Fraser University (SFU) - Department of Mathematics

Naveed Janjua

University of British Columbia (UBC) - School of Population and Public Health

Michael Otterstatter

University of British Columbia (UBC) - School of Population and Public Health

Mike Irvine

British Columbia Centre for Disease Control

Tyler Williamson

University of Calgary - Department of Community Health Sciences

Marie B. Varughese

Alberta Health Services - Analytics and Performance Reporting Branch

Michael Li

University of Alberta - Department of Mathematical and Statistical Sciences

Nathaniel Osgood

University of Saskatchewan - Department of Computer Science

David J. D. Earn

McMaster University - Department of Mathematics & Statistics

Beate Sander

University of Toronto - Toronto Health Economics and Technology Assessment (THETA)

Lauren E. Cipriano

University of Western Ontario - Richard Ivey School of Business

Kumar Murty

University of Toronto - Department of Mathematics

Fanyu Xiu

McGill University - Epidemiology, Biostatistics, & Occupational Health

Arnaud Godin

McGill University - Faculty of Medicine and Health Sciences

David L. Buckeridge

McGill University - School of Population and Global Health

Amy Hurford

Memorial University of Newfoundland (MNU) - Faculty of Science

Sharmistha Mishra

University of Toronto - Department of Medicine

Mathieu Maheu-Giroux

McGill University - Epidemiology, Biostatistics, & Occupational Health

Date Written: May 1, 2023

Abstract

Objectives: Mathematical modelling played an important role in the public health responses to the COVID-19 pandemic. The diverse epidemic trajectories and modelling approaches adopted by Canadian provinces provide a unique opportunity to understand factors that shaped modelling strategies. This study aims to summarize and analyze provincial COVID-19 modelling efforts across Canada.

Methods: We identified the main modelling teams with government mandates to model SARS- COV-2 in each province through referrals and membership in Canadian modelling networks. We included dynamic models used actively before December 2021. Information on models, data sources, and knowledge translation process were collected using standardized instruments.


Results: We obtained information on models from 6 provinces. For provinces with sustained community transmission, modelling focused on projecting epidemic indicators, healthcare demands, and evaluating impacts of proposed interventions. In provinces able to mitigate community transmission, models emphasized quantifying case importation risks. Most models were compartmental and deterministic, with horizons for projections of a few weeks. Models were continuously updated or replaced by new ones entirely, adapting to the changing local epidemics and requests from public health. Surveillance datasets for cases and hospitalizations, as well as serological studies were the main data sources for model calibration. Knowledge translation structure with decision-makers differed markedly between provinces.

Conclusion: Provincial modelling efforts during the COVID-19 pandemic were tailored to local contexts: chosen strategy (suppression/mitigation), epidemiological trajectories, and available resources. Strengthening of Canadian modelling capacity, developing and sustaining collaborations between modellers and governments, and earlier access to linked and timely surveillance data could help improve pandemic preparedness.

Note:
Funding Information: YX's research work is supported by Doctoral Research Awards from the Canadian Institutes of Health Research and Doctoral Research Awards from the Fonds de Recherche Québec – Santé.

Conflict of Interests: MM-G reports contractual arrangements from the Institut national de santé publique du Qué bec (INSPQ), the Institut d’excellence en santé et services sociaux (INESSS), the Public Health Agency of Canada, the World Health Organization, and the Joint United Nations Programme on HIV/AIDS (UNAIDS). Other co-authors report no COI.

Keywords: COVID-19; Mathematical modelling; Policy making; Knowledge translation; Pandemic; SARS-CoV-2

JEL Classification: I18, I1, I13

Suggested Citation

Xia, Yiqing and Anato, Jorge Luis Flores and Colijin, Caroline and Janjua, Naveed and Otterstatter, Michael and Irvine, Mike and Williamson, Tyler and Varughese, Marie B. and Li, Michael and Osgood, Nathaniel and Earn, David J. D. and Sander, Beate and Cipriano, Lauren E. and Murty, Kumar and Xiu, Fanyu and Godin, Arnaud and Buckeridge, David L. and Hurford, Amy and Mishra, Sharmistha and Maheu-Giroux, Mathieu, Canada's Provincial Covid-19 Pandemic Modelling Efforts: A Review of Mathematical Models and Their Impacts on the Responses (May 1, 2023). Available at SSRN: https://ssrn.com/abstract=4476949 or http://dx.doi.org/10.2139/ssrn.4476949

Yiqing Xia (Contact Author)

McGill University - Epidemiology, Biostatistics, & Occupational Health ( email )

Jorge Luis Flores Anato

McGill University - Epidemiology, Biostatistics, & Occupational Health

Caroline Colijin

Simon Fraser University (SFU) - Department of Mathematics

Naveed Janjua

University of British Columbia (UBC) - School of Population and Public Health ( email )

Michael Otterstatter

University of British Columbia (UBC) - School of Population and Public Health

Mike Irvine

British Columbia Centre for Disease Control

Tyler Williamson

University of Calgary - Department of Community Health Sciences ( email )

Marie B. Varughese

Alberta Health Services - Analytics and Performance Reporting Branch

Michael Li

University of Alberta - Department of Mathematical and Statistical Sciences

Nathaniel Osgood

University of Saskatchewan - Department of Computer Science ( email )

David J. D. Earn

McMaster University - Department of Mathematics & Statistics

Beate Sander

University of Toronto - Toronto Health Economics and Technology Assessment (THETA) ( email )

Eaton Building, 10th Floor, Room 247
200 Elizabeth Street
Toronto, M5G 2C4
Canada

Lauren E. Cipriano

University of Western Ontario - Richard Ivey School of Business

Kumar Murty

University of Toronto - Department of Mathematics

Fanyu Xiu

McGill University - Epidemiology, Biostatistics, & Occupational Health

Arnaud Godin

McGill University - Faculty of Medicine and Health Sciences

David L. Buckeridge

McGill University - School of Population and Global Health ( email )

Montreal, Quebec
Canada

Amy Hurford

Memorial University of Newfoundland (MNU) - Faculty of Science

Sharmistha Mishra

University of Toronto - Department of Medicine ( email )

Canada

Mathieu Maheu-Giroux

McGill University - Epidemiology, Biostatistics, & Occupational Health ( email )

845 Sherbrook Street West
Montreal, QC H3A 0G4
Canada

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