Analysis of SARS-CoV-2 transmission in airports based on real human close contact behaviors

Abstract

The COVID-19 pandemic has significantly impacted people's daily lives for over three years. Airports, with their dense population and frequent close contact, pose a higher risk of respiratory infectious diseases compared to many other indoor environments. However, limited availability of data on close contact behavior has resulted in a gap in indoor exposure analysis. This study conducted depth sensor measurements and video data collection across nine areas of a northern (airport A) and a southern (airport B) airports in China by 11 participants. The data, comprising more than 44 h of close contact behaviors, including interpersonal distance, relative facial orientation, and the relative position of individuals, were analyzed using a semi-supervised machine learning method. Based on this analysis, a close contact transmission model for COVID-19 was developed, which considers the aforementioned close contact behaviors to assess the risk of exposure and the efficacy of interventions. The average close contact ratio in 9 airport's areas is 25.4 % (ranging from 6.1 % to 55.0 %), with passengers having the highest frequency of close contact in manual check-in areas. During close contacts, the average interpersonal distance in airports is 1.2 m (ranging from 1.1 to 1.4 m), being shortest in boarding areas. Face-to-face close contact is highest in charging areas, with a percentage of 46.9 %. If people maintain a distance of over 1.0 m in all areas, the total virus exposure could be reduced by 6.9 %–22.0 % compared to the actual situation. Dining areas have the highest virus exposure risk for both short-range inhalation and mucosal deposition, followed by manual check-in areas. This study provides a data support for the scientific epidemic prevention and control in airports from the viewpoint of close contact behaviors.