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

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