Optimal Workplace Occupancy Strategies during the COVID-19 Pandemic

TL;DR

This paper develops a bi-objective optimization model to balance COVID-19 infection risk and productivity in workplaces, providing a practical framework and an online tool for organizations to determine optimal occupancy strategies.

Contribution

It introduces a novel probabilistic framework and optimization approach to identify Pareto optimal workplace occupancy strategies during the pandemic.

Findings

The model accurately estimates infection risk based on key parameters.

Numerical experiments demonstrate effective trade-offs between safety and productivity.

An online application helps organizations implement optimal occupancy strategies.

Abstract

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.