Application of a composite, multi-scale COVID-19 mitigation framework: US border use-case

Abstract

ABSTRACT Airborne pathogen transmission within crowded facilities can be modelled by combining several interrelated mechanisms of spread: movement of people, airflow dynamics, and aerosol dispersion. This paper describes a composite model framework combining analytical models to demonstrate the spread of an airborne pathogen in a crowded, confined space at an immigrant processing centre on the southern US border during the border crisis of March 2021. Recommendations that could reduce current COVID-19 infection rate from 11% to 6.16% at relatively low additional cost to the government are given. These recommendations could also lower the infection rate by approximately five times from 31.14% worst case from long indoor exposures down to 6.35% when immigrant processing times surge to 10 days. This work highlights the challenges of managing COVID-19 in crowded facilities, and provides quantitative decision options with potential both to slow and prevent disease spread, while lessening the economic burden on communities.