Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses

doi:10.17269/s41997-024-00910-9

Review

. 2024 Aug;115(4):541-557.

doi: 10.17269/s41997-024-00910-9. Epub 2024 Jul 25.

Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses

Yiqing Xia¹, Jorge Luis Flores Anato¹, Caroline Colijn², Naveed Janjua^{3

4}, Mike Irvine⁴, Tyler Williamson^{5

6}, Marie B Varughese^{7

8}, Michael Li⁸, Nathaniel Osgood⁹, David J D Earn^{10

11}, Beate Sander^{12

13

14

15}, Lauren E Cipriano^{16

17}, Kumar Murty¹⁸, Fanyu Xiu¹, Arnaud Godin¹⁹, David Buckeridge¹, Amy Hurford²⁰, Sharmistha Mishra^{12

21

22

23}, Mathieu Maheu-Giroux²⁴

Affiliations

¹ Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada.
² Department of Mathematics, Faculty of Science, Simon Fraser University, Burnaby, BC, Canada.
³ School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada.
⁴ British Columbia Centre for Disease Control (BCCDC), Vancouver, BC, Canada.
⁵ Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada.
⁶ Centre for Health Informatics, University of Calgary, Calgary, AB, Canada.
⁷ Analytics and Performance Reporting Branch, Alberta Health, Edmonton, AB, Canada.
⁸ Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada.
⁹ Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
¹⁰ Department of Mathematics & Statistics, McMaster University, Hamilton, ON, Canada.
¹¹ M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada.
¹² Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
¹³ Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.
¹⁴ Public Health Ontario, Toronto, ON, Canada.
¹⁵ ICES, Toronto, ON, Canada.
¹⁶ Ivey Business School, University of Western Ontario, London, ON, Canada.
¹⁷ Departments of Epidemiology & Biostatistics and Medicine, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, Canada.
¹⁸ Department of Mathematics, University of Toronto, Toronto, ON, Canada.
¹⁹ Department of Medicine, Faculty of Medicine and Health Science, McGill University, Montréal, QC, Canada.
²⁰ Department of Biology and Department of Mathematics and Statistics, Faculty of Science, Memorial University of Newfoundland and Labrador, St. John's, NL, Canada.
²¹ Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, ON, Canada.
²² MAP Centre for Urban Health Solutions, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.
²³ Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
²⁴ Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada. mathieu.maheu-giroux@mcgill.ca.

PMID: 39060710
PMCID: PMC11382646
DOI: 10.17269/s41997-024-00910-9

Review

Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses

Yiqing Xia et al. Can J Public Health. 2024 Aug.

. 2024 Aug;115(4):541-557.

doi: 10.17269/s41997-024-00910-9. Epub 2024 Jul 25.

Authors

Affiliations

¹ Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada.
² Department of Mathematics, Faculty of Science, Simon Fraser University, Burnaby, BC, Canada.
³ School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada.
⁴ British Columbia Centre for Disease Control (BCCDC), Vancouver, BC, Canada.
⁵ Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada.
⁶ Centre for Health Informatics, University of Calgary, Calgary, AB, Canada.
⁷ Analytics and Performance Reporting Branch, Alberta Health, Edmonton, AB, Canada.
⁸ Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada.
⁹ Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
¹⁰ Department of Mathematics & Statistics, McMaster University, Hamilton, ON, Canada.
¹¹ M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada.
¹² Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
¹³ Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.
¹⁴ Public Health Ontario, Toronto, ON, Canada.
¹⁵ ICES, Toronto, ON, Canada.
¹⁶ Ivey Business School, University of Western Ontario, London, ON, Canada.
¹⁷ Departments of Epidemiology & Biostatistics and Medicine, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, Canada.
¹⁸ Department of Mathematics, University of Toronto, Toronto, ON, Canada.
¹⁹ Department of Medicine, Faculty of Medicine and Health Science, McGill University, Montréal, QC, Canada.
²⁰ Department of Biology and Department of Mathematics and Statistics, Faculty of Science, Memorial University of Newfoundland and Labrador, St. John's, NL, Canada.
²¹ Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, ON, Canada.
²² MAP Centre for Urban Health Solutions, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.
²³ Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
²⁴ Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada. mathieu.maheu-giroux@mcgill.ca.

PMID: 39060710
PMCID: PMC11382646
DOI: 10.17269/s41997-024-00910-9

Abstract
in English, French

Setting: Mathematical modelling played an important role in the public health response to COVID-19 in Canada. Variability in epidemic trajectories, modelling approaches, and data infrastructure across provinces provides a unique opportunity to understand the factors that shaped modelling strategies.

Intervention: Provinces implemented stringent pandemic interventions to mitigate SARS-CoV-2 transmission, considering evidence from epidemic models. This study aimed to summarize provincial COVID-19 modelling efforts. We identified modelling teams working with provincial decision-makers, through referrals and membership in Canadian modelling networks. Information on models, data sources, and knowledge translation were abstracted using standardized instruments.

Outcomes: We obtained information from six provinces. For provinces with sustained community transmission, initial modelling efforts focused on projecting epidemic trajectories and healthcare demands, and evaluating impacts of proposed interventions. In provinces with low community transmission, models emphasized quantifying importation risks. Most of the models were compartmental and deterministic, with projection horizons of a few weeks. Models were updated regularly or replaced by new ones, adapting to changing local epidemic dynamics, pathogen characteristics, vaccines, and requests from public health. Surveillance datasets for cases, hospitalizations and deaths, and serological studies were the main data sources for model calibration. Access to data for modelling and the structure for knowledge translation differed markedly between provinces.

Implication: Provincial modelling efforts during the COVID-19 pandemic were tailored to local contexts and modulated by available resources. Strengthening Canadian modelling capacity, developing and sustaining collaborations between modellers and governments, and ensuring earlier access to linked and timely surveillance data could help improve pandemic preparedness.

RéSUMé: CONTEXTE: La modélisation mathématique a joué un rôle de premier plan dans les ripostes sanitaires à la COVID-19 au Canada. Les différentes trajectoires épidémiques provinciales, leurs approches de modélisation et infrastructures de données représentent une occasion unique de comprendre les facteurs qui ont influencé les stratégies de modélisation provinciales. INTERVENTION: Les provinces ont mis en place des mesures de santé publique strictes afin d’atténuer la transmission du SRAS-CoV-2 en tenant compte des données probantes provenant des modèles épidémiques. Notre étude vise à décrire et résumer les efforts provinciaux de modélisation de la COVID-19. Nous avons identifié les équipes de modélisation travaillant avec les décideurs provinciaux parmi les réseaux Canadiens de modélisation et par référence. Les informations sur les modèles, leurs sources de données et les approches de mobilisation des connaissances ont été obtenues à l’aide d’instruments standardisés. RéSULTATS: Nous avons colligé les informations provenant de six provinces. Pour les provinces qui ont eu de la transmission communautaire soutenue, les efforts de modélisation initiaux se sont concentrés sur la projection des trajectoires épidémiques et des demandes de soins de santé et sur l’évaluation des impacts des interventions proposées. Dans les provinces où la transmission communautaire a été faible, les modèles visaient à quantifier les risques d’importation. La plupart des équipes ont développé des modèles à compartiments déterministes avec des horizons de projection de quelques semaines. Les modèles ont été régulièrement mis à jour ou remplacés par de nouveaux, s’adaptant aux dynamiques locales, à l’arrivée de nouveaux variants, aux vaccins et aux demandes des autorités de santé publique. Les données de surveillance des cas, des hospitalisations et des décès, ainsi que les études sérologiques, ont constitué les principales sources de données pour calibrer les modèles. L’accès aux données pour la modélisation et la structure de mobilisation des connaissances différaient considérablement d’une province à l’autre. IMPLICATION: Les efforts de modélisation provinciaux pendant la pandémie de la COVID-19 ont été adaptés aux contextes locaux et modulés par les ressources disponibles. Le renforcement de la capacité canadienne de modélisation, le développement et le maintien de collaborations entre les modélisateurs et les gouvernements, ainsi qu’un accès rapide et opportun aux données de surveillance individuelles et liées pourraient contribuer à améliorer la préparation aux futures pandémies.

Keywords: COVID-19; Knowledge translation; Mathematical modelling; Pandemic; Policymaking; SARS-CoV-2.

PubMed Disclaimer

Conflict of interest statement

MM-G reports contractual arrangements from the Institut national de santé publique du Québec (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).

Figures

**Fig. 1**
Evolution of models over time and the main reasons for change in selected provinces. Only models with detailed information on their evolution are shown. *ABM* Agent-based model; *INESSS* Institut national d’excellence en santé et services sociaux; *INSPQ* Institut national de santé publique du Québec; *SEAIR* Susceptible-Exposed-Asymptomatic-Infected-Recovered; *UofT* University of Toronto; *LHSC* London Health Sciences Centre

**Fig. 2**
Structure of provincial modelling teams and the communication of evidence to decision-makers and the public in Saskatchewan, Ontario, and Québec during the COVID-19 pandemic

See this image and copyright information in PMC

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References

1. Aleman, D. M., Tham, B. Z., Wagner, S. J., Semelhago, J., Mohammadi, A., Price, P., Giffen, R. & Rahman, P. (2021). How effective was Newfoundland & Labrador’s travel ban to prevent the spread of COVID-19? An agent-based analysis. medRxiv, 2021-02.
1. Allen, M., Spencer, A., Gibson, A., Matthews, J., Allwood, A., Prosser, S., & Pitt, M. (2015). Right cot, right place, right time: Improving the design and organisation of Neonatal Care Networks – a computer simulation study. Health Services and Delivery Research, 3(20), 1–128. 10.3310/hsdr03200 - PubMed
1. Allin, S., Fitzpatrick, T., Marchildon, G. P., & Quesnel-Vallée, A. (2022). The federal government and Canada’s COVID-19 responses: From ‘we’re ready, we’re prepared’ to ‘fires are burning.’ Health Economics, Policy and Law,17(1), 76–94. 10.1017/S1744133121000220 10.1017/S1744133121000220 - DOI - PMC - PubMed
1. Barrett, K., Khan, Y. A., Mac, S., Ximenes, R., Naimark, D. M. J., & Sander, B. (2020). Estimation of COVID-19-induced depletion of hospital resources in Ontario, Canada. CMAJ, 192(24), E640–E646. 10.1503/cmaj.200715 10.1503/cmaj.200715 - DOI - PMC - PubMed
1. BC COVID-19 Modelling Group. (n.d.) [Website]. Retrieved 24 Mar 2023 from https://bccovid-19group.ca

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[1] Aleman, D. M., Tham, B. Z., Wagner, S. J., Semelhago, J., Mohammadi, A., Price, P., Giffen, R. & Rahman, P. (2021). How effective was Newfoundland & Labrador’s travel ban to prevent the spread of COVID-19? An agent-based analysis. medRxiv, 2021-02.

[2] Aleman, D. M., Tham, B. Z., Wagner, S. J., Semelhago, J., Mohammadi, A., Price, P., Giffen, R. & Rahman, P. (2021). How effective was Newfoundland & Labrador’s travel ban to prevent the spread of COVID-19? An agent-based analysis. medRxiv, 2021-02.

[3] Allen, M., Spencer, A., Gibson, A., Matthews, J., Allwood, A., Prosser, S., & Pitt, M. (2015). Right cot, right place, right time: Improving the design and organisation of Neonatal Care Networks – a computer simulation study. Health Services and Delivery Research, 3(20), 1–128. 10.3310/hsdr03200 - PubMed

[4] Allen, M., Spencer, A., Gibson, A., Matthews, J., Allwood, A., Prosser, S., & Pitt, M. (2015). Right cot, right place, right time: Improving the design and organisation of Neonatal Care Networks – a computer simulation study. Health Services and Delivery Research, 3(20), 1–128. 10.3310/hsdr03200 - PubMed

[5] Allin, S., Fitzpatrick, T., Marchildon, G. P., & Quesnel-Vallée, A. (2022). The federal government and Canada’s COVID-19 responses: From ‘we’re ready, we’re prepared’ to ‘fires are burning.’ Health Economics, Policy and Law,17(1), 76–94. 10.1017/S1744133121000220 10.1017/S1744133121000220 - DOI - PMC - PubMed

[6] Allin, S., Fitzpatrick, T., Marchildon, G. P., & Quesnel-Vallée, A. (2022). The federal government and Canada’s COVID-19 responses: From ‘we’re ready, we’re prepared’ to ‘fires are burning.’ Health Economics, Policy and Law,17(1), 76–94. 10.1017/S1744133121000220 10.1017/S1744133121000220 - DOI - PMC - PubMed

[7] Barrett, K., Khan, Y. A., Mac, S., Ximenes, R., Naimark, D. M. J., & Sander, B. (2020). Estimation of COVID-19-induced depletion of hospital resources in Ontario, Canada. CMAJ, 192(24), E640–E646. 10.1503/cmaj.200715 10.1503/cmaj.200715 - DOI - PMC - PubMed

[8] Barrett, K., Khan, Y. A., Mac, S., Ximenes, R., Naimark, D. M. J., & Sander, B. (2020). Estimation of COVID-19-induced depletion of hospital resources in Ontario, Canada. CMAJ, 192(24), E640–E646. 10.1503/cmaj.200715 10.1503/cmaj.200715 - DOI - PMC - PubMed

[9] BC COVID-19 Modelling Group. (n.d.) [Website]. Retrieved 24 Mar 2023 from https://bccovid-19group.ca

[10] BC COVID-19 Modelling Group. (n.d.) [Website]. Retrieved 24 Mar 2023 from https://bccovid-19group.ca

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Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses

Affiliations

Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses

Authors

Affiliations

Abstract
in English, French

Conflict of interest statement

Figures

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Cited by

References

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Medical

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Abstract in English, French

Conflict of interest statement

Figures

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Cited by

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Medical

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Abstract
in English, French