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.

Methods
    We use https://pubmed.ncbi.nlm.nih.gov/18366252/ to create a contact distribution as a probability density function

    then generate the required number of persons. People are placed randomly in the unit square and connect to a

    number (drawn from the contact distribution) of closest neighbors. The people can then infect each contact with

    probability T_p. The simulation is run (for various values of T_p) while tracking the number of new infections per day until there are no remaining infected participants.