As of July 5th, 2020, there have been over 11.5 million worldwide cases of COVID-19, including over 100 000 in Ontario. Many groups have taken different approaches to modeling the spread of this pandemic. We propose our approach to modelling COVID-19 in Ontario, extending the classic SIR compartmental model by introducing three novel components. First, we incorporate age stratification into our population structure, distinguishing between 0-19 year olds, 20-59 year olds, and individuals aged 60+. Secondly, we apply the age stratified contact rate matrix for Canada found by Prem, Cook, & Jit (2017) to our model. Lastly, we introduce several additional compartments to the SEIR model, distinguishing between presymptomatic, asymptomatic and symptomatic infectious individuals. Using a derivative-free optimization algorithm, we solve for the optimal effective contact rate to fit our model to reported cases of COVID-19 in Ontario by (italicized) case onset date, as reported by Ontario's integrated Public Health Information System (iPHIS). Last but not least, we explore the use of Google Mobility data to infer its effect on contact rates under social-distancing guidelines. As individuals' mobility changes through various phases of containment measures (lockdown, stage1, stage 2, etc.) we explore the change on pathogen transmission as influenced by other factors such as mask use/wear requirements, bans on social gatherings, reduced social bubbles, working from home policies, etc.

Authors: Fields, R., Humphrey, L., Nahirniak, M., Flynn-Primrose, D., Thommes, E. W., Cojocaru, M. G.

Presenting author: Roie Fields, University of Guelph

Authors affiliation: Fields, Humphrey, Nahirniak, Flynn-Primrose, Cojocaru - Math & Stats, Univ. of Guelph

Thommes - Sanofi Pasteur