  During the COVID-19 pandemic, many organizations (e.g. businesses, companies,
government facilities, etc.) have attempted to reduce infection risk by
employing partial home office strategies. However, working from home can also
reduce productivity for certain types of work and particular employees. Over
the long term, many organizations therefore face a need to balance infection
risk against productivity. Motivated by this trade-off, we model this situation
as a bi-objective optimization problem and propose a practical approach to find
trade-off (Pareto optimal) solutions. We present a new probabilistic framework
to compute the expected number of infected employees as a function of key
parameters including: the incidence level in the neighborhood of the
organization, the COVID-19 transmission rate, the number of employees, the
percentage of vaccinated employees, the testing frequency, and the contact rate
among employees. We implement the model and the optimization algorithm and
perform several numerical experiments with different parameter settings.
Furthermore, we provide an online application based on the models and
algorithms developed in this paper, which can be used to identify the optimal
workplace occupancy rate for real-world organizations.
