Scheduling Mechanisms to Control Spread of Covid-19 (Simulation Results)

We study scheduling mechanisms that explore the trade-off between
containing the spread of COVID-19 and performing in-person activity in
organizations. Our mechanisms, referred to as group
scheduling, are based on partitioning the
population randomly into groups and scheduling each
group on appropriate days with possible gaps (when no one is working and
all are quarantined). Each group interacts with no other group and,
importantly, any person who is symptomatic in a group is quarantined. We
show that our mechanisms effectively trade-off in-person activity for more
effective control of the COVID-19 virus spread. In particular, we show
that a mechanism which partitions the population into two groups that
alternatively work in-person for five days each, flatlines the number of
COVID-19 cases quite effectively, while still maintaining in-person
activity at 70% of pre-COVID-19 level. Other mechanisms that partitions
into two groups with less continuous work days or more spacing or three
groups achieve even more aggressive control of the virus at the cost of a
somewhat lower in-person activity (about 50%). We demonstrate the efficacy
of our mechanisms by theoretical analysis and extensive experimental
simulations on various epidemiological models based on real-world data.