Canada's Provincial Covid-19 Pandemic Modelling Efforts: A Review of Mathematical Models and How They Informed Public Health Responses

Posted: 5 Dec 2023 Last revised: 23 Dec 2023

See all articles by Yiqing Xia

Yiqing Xia

McGill University - Epidemiology, Biostatistics, & Occupational Health

Jorge Luis Flores Anato

McGill University

Caroline Colijn

Simon Fraser University

Naveed Janjua

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

Michael Irvine

BC Centre for Disease Control

Tyler Williamson

University of Calgary - Department of Community Health Sciences

Marie B. Varughese

University of Alberta

Michael Li

University of Alberta

Nathaniel Osgood

University of Saskatchewan

David J.D. Earn

McMaster University

Beate Sander

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

Lauren E. Cipriano

Ivey Business School

V. Kumar Murty

University of Toronto - Department of Mathematics

Fanyu Xiu

McGill University - Epidemiology, Biostatistics, & Occupational Health

Arnaud Godin

McGill University - School of Population and Global Health

David L. Buckeridge

McGill University - School of Population and Global Health

Amy Hurford

Memorial University

Sharmistha Mishra

University of Toronto - Department of Medicine

Mathieu Maheu-Giroux

McGill University - Epidemiology, Biostatistics, & Occupational Health

Abstract

Canada has a federated public health system (10 provinces and 3 territories), meaning that the COVID-19 pandemic response was largely decentralized and supported by regional modelling. The diversity in epidemic trajectories and modelling approaches adopted by Canadian provinces provide a unique opportunity to understand factors that shaped regional modelling strategies. This study aims to summarize and analyze provincial COVID-19 modelling efforts across Canada.

Through referrals and membership in Canadian modelling networks, we identified the main teams that modelled SARS-CoV-2 transmission in collaboration with provincial/territorial decision-makers. We included transmission dynamics and health systems models used between March 2020­-December 2021 (i.e., the height of the public health emergency). Information on models, data sources, and knowledge translation processes were collected using standardized data-collection instruments.

We obtained information on 20 models used across 6 provinces. In provinces with sustained community transmission, modelling focused on projecting epidemic indicators, healthcare demands, and evaluating impacts of proposed interventions. In provinces able to eliminate community transmission, models emphasized quantifying case importation risks. Most models were compartmental (N=13/20) and deterministic (N=11), with short projection horizons of a few weeks. Models were continuously updated or replaced in response to changes in local epidemiology and interventions (e.g., variant emergence, vaccines) and requests from public health decision-makers (e.g., more granular outputs, optimal vaccination strategies). In most provinces (N=5/6), teams accessed data through individual data-sharing agreements with governments, but in Ontario a data-sharing mechanism was mediated by a third party. Structures for collaboration and knowledge-translation differed markedly between provinces: teams either had frequent contact with decision-makers, liaised through a designated person, or relied on in-house modelling with academic modellers as advisors.

Regional modelling efforts during the COVID-19 pandemic were tailored to the local context: chosen strategy (suppression/mitigation), epidemiological trajectories, and available resources. Growing and diversifying modelling capacity across Canada, developing and sustaining collaborations between modellers and decision-makers, and better integration of models with timely surveillance data, could strengthen the contribution of modelling to decentralized pandemic preparedness and response in Canada. These lessons are applicable to other jurisdictions and can inform and strengthen infectious disease modelling.

Note: This conference abstract was presented at the 9th International Conference on Infectious Disease Dynamics organized by the journal Epidemics. This abstract has not been screened by SSRN for potential for public harm and should not be used to inform any clinical decision making. No competing interests or funding statements have been declared.

Keywords: COVID-19,SARS-CoV-2,mathematical modelling,pandemic response

Suggested Citation

Xia, Yiqing and Flores Anato, Jorge Luis and Colijn, Caroline and Janjua, Naveed and Irvine, Michael 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, V. 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 How They Informed Public Health Responses. 9TH INTERNATIONAL CONFERENCE ON INFECTIOUS DISEASE DYNAMICS:P2.041, Available at SSRN: https://ssrn.com/abstract=4654927

Yiqing Xia

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

Jorge Luis Flores Anato (Contact Author)

McGill University ( email )

1001 Sherbrooke St. W
Montreal, Quebec H3A 1G5
Canada

Caroline Colijn

Simon Fraser University ( email )

Naveed Janjua

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

Michael Irvine

BC Centre for Disease Control

655 W 12th Avenue
Vancouver, V5Z 4R4
Canada

Tyler Williamson

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

Marie B. Varughese

University of Alberta ( email )

Michael Li

University of Alberta ( email )

Nathaniel Osgood

University of Saskatchewan ( email )

David J.D. Earn

McMaster University ( email )

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

Ivey Business School ( email )

V. Kumar Murty

University of Toronto - Department of Mathematics ( email )

Toronto, Ontario M5S 3G3
Canada

Fanyu Xiu

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

845 Sherbrook Street West
Montreal, QC H3A 0G4
Canada

Arnaud Godin

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

David L. Buckeridge

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

Montreal, Quebec
Canada

Amy Hurford

Memorial University ( email )

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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