The Interplay Between COVID-19 and the Economy in Canada

Matheus Grasselli

We propose a generalized Susceptible-Exposed-Infected-Removed (SEIR) model to track COVID-19 in Canadian provinces taking into account the impact of the pandemics on unemployment. The model is based on a network representing provinces, where the contact between individuals from different locations is defined by a data-driven mixing matrix. Moreover, we use time-dependent parameters to account for the dynamical evolution of the disease incidence, as well as changes in the rates of hospitalization, intensive care unit (ICU) admission, and death. Unemployment is accounted for as a reduction in the social interaction, which translates into smaller transmission parameters. Conversely, the model assumes that higher proportions of infected individuals reduce overall economic activity and therefore increase unemployment. We test the model using publicly available sources and find that it is able to reproduce the reported data with remarkable in-sample accuracy. We also test the model’s ability to make short-term out-of-sample forecasts and find it very satisfactory, except in periods of rapid changes in behavior. Finally, we present long-term predictions for both epidemiological and economic variables under several future vaccination scenarios. (This is joint work with Vinicius Albani, Weijie Pang, and Jorge P. Zubelli)  

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