Evaluation of infection probability of Covid-19 in different types of airliner cabins

• Published in: Building and environment Vol. 234; p. 110159
• Main Authors: Wang, Feng, Zhang, Tengfei (Tim), You, Ruoyu, Chen, Qingyan
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.04.2023; Published by Elsevier Ltd
• Subjects: Air distribution; Computational fluid dynamics (CFD); Economy-class cabin; Experimental validation; Infectious disease; Wells-Riley model; Infectious disease; Experimental validation; Wells-Riley model; Economy-class cabin; Air distribution; Computational fluid dynamics (CFD)
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2023.110159

According to the World Health Organization (https://covid19.who.int/), more than 651 million people have been infected by COVID-19, and more than 6.6 million of them have died. COVID-19 has spread to almost every country in the world because of air travel. Cases of COVID-19 transmission from an index patient to fellow passengers in commercial airplanes have been widely reported. This investigation used computational fluid dynamics (CFD) to simulate airflow and COVID-19 virus (SARS-CoV-2) transport in a variety of airliner cabins. The cabins studied were economy-class with 2-2, 3-3, 2-3-2, and 3-3-3 seat configurations, respectively. The CFD results were validated by using experimental data from a seven-row cabin mockup with a 3-3 seat configuration. This study used the Wells-Riley model to estimate the probability of infection with SARS-CoV-2. The results show that CFD can predict airflow and virus transmission with acceptable accuracy. With an assumed flight time of 4 h, the infection probability was almost the same among the different cabins, except that the 3-3-3 configuration had a lower risk because of its airflow pattern. Flying time was the most important parameter for causing the infection, while cabin type also played a role. Without mask wearing by the passengers and the index patient, the infection probability could be 8% for a 10-h, long-haul flight, such as a twin-aisle air cabin with 3-3-3 seat configuration. •Proposed a CFD based method to study airborne infectious diseases in an air cabin.•Validated the CFD results by experimental data obtained from a cabin mockup.•Evaluated infection probabilities in different airplanes and seats to COVID-19.•Found flight time is the most important factor in the infection probability.