Airborne SARS-CoV-2 in aircraft cabins: New inactivation data significantly influences infection risk predictions

Abstract

We predict the SARS-CoV-2 infection risk in aircraft cabins by simulating the aerosol transport with computational fluid dynamics and taking medical parameters into account. A recently presented new measurement technique allows to measure the rapid virus inactivation after exhalation with high temporal resolution. In addition, much higher airborne SARS-CoV-2 inactivation rates than in previous studies were obtained. This raises the question of how the new knowledge of SARS-CoV-2 stability affects the prediction of infection risk. To answer this question, we evaluated 70 Lagrangian particle simulations with an index person sitting in all possible seats in an aircraft cabin. We then estimated the infection risk for the other passengers based on the old and new SARS-CoV-2 stability data. For typical transmission events, we found that the predicted infection risk is reduced by about 50 % for the new stability data at low CO2 (500 ppm). However, elevated ambient CO2 concentrations of 3000 ppm protect the virus from inactivation and increase infection risk by about 50 % compared to low CO2.