Symptom-Based Isolation Policies: Evidence from a Mathematical Model of Outbreaks of Influenza and COVID-19

Adam Burns; Alexander Gutfraind

A relatively novel aspect of our model is the use of compartments to stratify the population by both the day of their infection, their location (un-isolated in community vs. isolated at home) and their cohort (see Appendix A for details). The day of infection determines the rate of virus shedding and the probability of symptoms, which then influences the decision to stay at home or to return. Generally, persons in the model were assumed to return to their community when their symptoms became less severe. The probability of returning was based on the stage of the illness, as well as the isolation policy. The isolation policy had a considerable effect on preventing premature return during the last days of illness, but also during the middle of the course of illness on days when symptoms temporarily waned. We allowed a vaccine to be received by some, if the vaccine is available ahead of the outbreak, attaining partial protection against the pathogen. The population was structured into cohorts representing ages in a school. In schools, students in the same grade generally have closer contacts to peers in the same grade, as opposed to the students in the other grades who also tend to be older or younger [41, 42] .