[Canadian Journal of Public Health, Editorial, 26 July 2024]

This issue of the Journal includes an important paper by Xia and colleagues (Xia et al., 2024 ) describing recent pandemic experiments modeling the disease. Throughout the pandemic, health officials and government leaders showed graphs of projected epidemic curves, and alternative curves depending on whether a particular intervention like physical distancing or school closures was implemented.

Underlying these projections are computer simulation models. Xia et al. ( 2024 ) surveyed and characterized the variety of these models for six provinces where information was available. There was some informal information sharing among these modelers and regular cross-country virtual conversations agreed by the Public Health Agency of Canada (PHAC). Still, important lessons merit being more widely shared, for which this CJPH article provides an important starting point.

As in all models, the veracity of the projections depends critically on the quality of the data on which they are based, and the methodologies applied. Canada is well endowed with infectious disease modeling expertise, primarily based in universities. However, the data needed to support these modeling efforts were too often limited. In many cases, the data does not exist; for example, at the start of the pandemic, accurate counts of new infections, hospitalizations, and deaths due to COVID, as well as the availability of ventilators and personal protective equipment (PPE) were unavailable.

In other cases, the data existed digitally but were not shared, even within provinces and the same or closely affiliated agencies. For example, data in one agency on infected individuals could have been linked to data in another agency that was capturing the virus' genotype at the individual level, but were not shared—thus depriving modelers of critical information on the virulence of evolving mutations. Another example was the failure to link detailed individual-level data on infections, vaccinations, and hospital admissions within the same province. Doing so would have enabled inputs and analysis greatly improving mathematical modeling.

Notwithstanding repeated claims that health care is a provincial responsibility, the federal government constitutionally has important powers as well and plays an overarching and coordinating role as health is a shared jurisdiction. Specifically for health crises like the pandemic, the Constitution grants the federal government powers regarding quarantine.

The federal government played a major role in procuring vaccines and test kits, and providing large cash transfers to individuals and businesses to help them bear the economic hardships of lockdowns and other measures intended to stem pandemic spread. Statistics Canada's legislated authorities and flexible data collection capabilities were available, though underutilized.

The federally funded COVID-19 Immunity Task Force (COVID-19 Immunity Task Force, nd ) played a key role, including producing seroprevalence estimates, albeit mostly pulling data from various existing sources including blood donations, cohort studies, and lab tests, sources that were not designed to provide unbiased surveillance. The Canadian COVID-19 Antibody and Health Survey (CCAHS) (Statistics Canada, 2023 ) made extensive efforts to provide unbiased sampling, an area that merits major improvements.

PHAC further has important responsibilities regarding infectious disease outbreaks, including modeling. This and other information are required to brief the federal Minister of Health and Cabinet on how the pandemic was likely to evolve. However, even though the provinces signed agreements more than a decade ago to share key data, such as individual-level data on cases of infection, these data often did not flow, or were seriously incomplete…

Citerank: (15) 679712CANMOD – PeopleCANMOD is a national network, with members located across the country and associated with a broader Emerging Infectious Disease Modelling (EIDM) initiative. We are a community of modellers, statisticians, epidemiologists, public health decision-makers, and those implementing and delivering interventions.10019D3ABAB, 679752Amy HurfordAmy Hurford is an Associate Professor jointly appointed in the Department of Biology and the Department of Mathematics and Statistics at Memorial University of Newfoundland and Labrador. 10019D3ABAB, 679757Beate SanderCanada Research Chair in Economics of Infectious Diseases and Director, Health Modeling & Health Economics and Population Health Economics Research at THETA (Toronto Health Economics and Technology Assessment Collaborative).10019D3ABAB, 679761Caroline ColijnDr. Caroline Colijn works at the interface of mathematics, evolution, infection and public health, and leads the MAGPIE research group. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial College London's Centre for the Mathematics of Precision Healthcare.10019D3ABAB, 679775David BuckeridgeDavid is a Professor in the School of Population and Global Health at McGill University, where he directs the Surveillance Lab, an interdisciplinary group that develops, implements, and evaluates novel computational methods for population health surveillance. He is also the Chief Digital Health Officer at the McGill University Health Center where he directs strategy on digital transformation and analytics and he is an Associate Member with the Montreal Institute for Learning Algorithms (Mila).10019D3ABAB, 679776David EarnProfessor of Mathematics and Faculty of Science Research Chair in Mathematical Epidemiology at McMaster University.10019D3ABAB, 679844Mathieu Maheu-GirouxCanada Research Chair (Tier 2) in Population Health Modeling and Associate Professor, McGill University.10019D3ABAB, 679855Nathaniel OsgoodNathaniel D. Osgood is a Professor in the Department of Computer Science and Associate Faculty in the Department of Community Health & Epidemiology at the University of Saskatchewan.10019D3ABAB, 679856Naveed Zafar JanjuaDr. Naveed Zafar Janjua is an epidemiologist and senior scientist at the BC Centre for Disease Control and Clinical Associate Professor at School of Population and Public Health, University of British Columbia. Dr. Janjua is a Medical Doctor (MBBS) with a Masters of Science (MSc) degree in Epidemiology & Biostatistics and Doctorate in Public Health (DrPH). 10019D3ABAB, 679880Sharmistha MishraSharmistha Mishra is an infectious disease physician and mathematical modeler and holds a Tier 2 Canadian Research Chair in Mathematical Modeling and Program Science.10019D3ABAB, 679893Kumar MurtyProfessor Kumar Murty is in the Department of Mathematics at the University of Toronto. His research fields are Analytic Number Theory, Algebraic Number Theory, Arithmetic Algebraic Geometry and Information Security. He is the founder of the GANITA lab, co-founder of Prata Technologies and PerfectCloud. His interest in mathematics ranges from the pure study of the subject to its applications in data and information security.10019D3ABAB, 685387Michael Y LiProfessor of Mathematics in the Department of Mathematical and Statistical Sciences at the University of Alberta, and Director of the Information Research Lab (IRL).10019D3ABAB, 701020CANMOD – PublicationsPublications by CANMOD Members144B5ACA0, 701037MfPH – Publications144B5ACA0, 704045Covid-19859FDEF6

[Canadian Journal of Public Health, 25 July 2024]

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.