TY - GEN
T1 - Incorporating Pedestrian Movement in Computational Models of COVID-19 Spread during Air-travel
AU - Wu, Yuxuan
AU - Namilae, Sirish
AU - Mubayi, Anuj
AU - Scotch, Matthew
AU - Srinivasan, Ashok
N1 - Funding Information:
We acknowledge the support of NSF grant 1931483 and NIH grant R15LM013382. SN acknowledges the support of DOT through UTC-Center for Advanced Transportation Mobility (CATM).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - COVID-19 pandemic has resulted in an over 60 % reduction in airtravel worldwide according to some estimates. The high economic and public perception costs of potential superspreading during air-travel necessitates research efforts that model, explain and mitigate disease spread. The long-duration exposure to infected passengers and the limited air circulation in the cabin are considered to be responsible for the infection spread during flight. Consequently, recent public health measures are primarily based on these aspects. However, a survey of recent on-flight outbreaks indicates that some aspects of the COVID-19 spread, such as long-distance superspreading, cannot be explained without also considering the movement of people. Another factor that could be influential but has not gained much attention yet is the unpredictable passenger behavior. Here, we use a novel infection risk model that is linked with pedestrian dynamics to accurately capture these aspects of infection spread. The model is parameterized through spatiotemporal analysis of a recent superspreading event in a restaurant in China. The passenger movement during boarding and deplaning, as well as the in-plane movement, are modeled with social force model and agent-based model respectively. We utilize the model to evaluate what-if scenarios on the relative effectiveness of policies and procedures such as masking, social distancing, as well as synergistic effects by combining different approaches in airplanes and other contexts. We find that in certain instances independent strategies can combine synergistically to reduce infection probability, by more than a sum of individual strategies.
AB - COVID-19 pandemic has resulted in an over 60 % reduction in airtravel worldwide according to some estimates. The high economic and public perception costs of potential superspreading during air-travel necessitates research efforts that model, explain and mitigate disease spread. The long-duration exposure to infected passengers and the limited air circulation in the cabin are considered to be responsible for the infection spread during flight. Consequently, recent public health measures are primarily based on these aspects. However, a survey of recent on-flight outbreaks indicates that some aspects of the COVID-19 spread, such as long-distance superspreading, cannot be explained without also considering the movement of people. Another factor that could be influential but has not gained much attention yet is the unpredictable passenger behavior. Here, we use a novel infection risk model that is linked with pedestrian dynamics to accurately capture these aspects of infection spread. The model is parameterized through spatiotemporal analysis of a recent superspreading event in a restaurant in China. The passenger movement during boarding and deplaning, as well as the in-plane movement, are modeled with social force model and agent-based model respectively. We utilize the model to evaluate what-if scenarios on the relative effectiveness of policies and procedures such as masking, social distancing, as well as synergistic effects by combining different approaches in airplanes and other contexts. We find that in certain instances independent strategies can combine synergistically to reduce infection probability, by more than a sum of individual strategies.
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U2 - 10.1109/AERO53065.2022.9843497
DO - 10.1109/AERO53065.2022.9843497
M3 - Conference contribution
AN - SCOPUS:85137585022
T3 - IEEE Aerospace Conference Proceedings
BT - 2022 IEEE Aerospace Conference, AERO 2022
PB - IEEE Computer Society
T2 - 2022 IEEE Aerospace Conference, AERO 2022
Y2 - 5 March 2022 through 12 March 2022
ER -