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1.
Transp Policy (Oxf) ; 119: 32-44, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1702657

ABSTRACT

The COVID-19 pandemic has devastated the air transport industry, forcing airlines to take measures to ensure the safety of passengers and crewmembers. Among the many protective measures, mask mandate onboard the airplane is an important one, but travelers' mask-wearing intentions during flight remain uninvestigated especially in the US where mask use is a topic of on-going debate. This study focused on the mask use of airline passengers when they fly during COVID-19, using the theory of planned behavior (TPB) model to examine the relationship between nine predicting factors and the mask-wearing intention in the aircraft cabin. A survey instrument was developed to collect data from 1124 air travelers on Amazon Mechanical Turk (MTurk), and the data was statistically analyzed using structural equation modeling and logistic regression. Results showed that attitude, descriptive norms, risk avoidance, and information seeking significantly influenced the travelers' intention to wear a mask during flight in COVID-19. Group analysis further indicated that the four factors influenced mask-wearing intentions differently on young, middle-aged, and senior travelers. It was also found that demographic and travel characteristics including age, education, income, and travel frequency can be used to predict if the airline passenger was willing to pay a large amount to switch to airlines that adopted different mask policies during COVID-19. The findings of this study fill the research gap of air travelers' intentions to wear a mask when flying during a global pandemic and provide recommendations for mask-wearing policies to help the air transport industry recover from COVID-19.

2.
Transport policy ; 2022.
Article in English | EuropePMC | ID: covidwho-1679062

ABSTRACT

The COVID-19 pandemic has devastated the air transport industry, forcing airlines to take measures to ensure the safety of passengers and crewmembers. Among the many protective measures, mask mandate onboard the airplane is an important one, but travelers' mask-wearing intentions during flight remain uninvestigated especially in the US where mask use is a topic of on-going debate. This study focused on the mask use of airline passengers when they fly during COVID-19, using the theory of planned behavior (TPB) model to examine the relationship between nine predicting factors and the mask-wearing intention in the aircraft cabin. A survey instrument was developed to collect data from 1124 air travelers on Amazon Mechanical Turk (MTurk), and the data was statistically analyzed using structural equation modeling and logistic regression. Results showed that attitude, descriptive norms, risk avoidance, and information seeking significantly influenced the travelers' intention to wear a mask during flight in COVID-19. Group analysis further indicated that the four factors influenced mask-wearing intentions differently on young, middle-aged, and senior travelers. It was also found that demographic and travel characteristics including age, education, income, and travel frequency can be used to predict if the airline passenger was willing to pay a large amount to switch to airlines that adopted different mask policies during COVID-19. The findings of this study fill the research gap of air travelers’ intentions to wear a mask when flying during a global pandemic and provide recommendations for mask-wearing policies to help the air transport industry recover from COVID-19.

3.
PLoS One ; 15(7): e0235891, 2020.
Article in English | MEDLINE | ID: covidwho-725974

ABSTRACT

There is direct evidence for the spread of infectious diseases such as influenza, SARS, measles, and norovirus in locations where large groups of people gather at high densities e.g. theme parks, airports, etc. The mixing of susceptible and infectious individuals in these high people density man-made environments involves pedestrian movement which is generally not taken into account in modeling studies of disease dynamics. We address this problem through a multiscale model that combines pedestrian dynamics with stochastic infection spread models. The pedestrian dynamics model is utilized to generate the trajectories of motion and contacts between infected and susceptible individuals. We incorporate this information into a stochastic infection dynamics model with infection probability and contact radius as primary inputs. This generic model is applicable for several directly transmitted diseases by varying the input parameters related to infectivity and transmission mechanisms. Through this multiscale framework, we estimate the aggregate numbers and probabilities of newly infected people for different winding queue configurations. We find that the queue configuration has a significant impact on disease spread for a range of infection radii and transmission probabilities. We quantify the effectiveness of wall separators in suppressing the disease spread compared to rope separators. Further, we find that configurations with short aisles lower the infection spread when rope separators are used.


Subject(s)
Communicable Disease Control , Communicable Diseases/transmission , Crowding , Pedestrians , Computer Simulation , Contact Tracing , Humans , Probability , Stochastic Processes
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