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

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