Cost and social distancing dynamics in a mathematical model of COVID-19 with application to Ontario, Canada

A mathematical model of COVID-19 is presented where the decision to increase or decrease social distancing is modelled dynamically as a function of the measured active and total cases as well as the perceived cost of isolating. Along with the cost of isolation, we define a healthcare cost and a total cost. We explore these costs by adjusting parameters that could change with policy decisions. We observe that minimum costs are not always associated with increased spending and increased vigilance which is due to the desire for people to not distance and the fatigue they experience when they do. We demonstrate that an increased in the number of lock-downs, each of shorter duration can lead to minimal costs. Our results are compared to case data in Ontario, Canada from March to August 2020 and details of extracting the results to other regions is presented.