10 - Safety and Security
A MODEL OF DISEASE TRANSMISSION AND MITIGATION OPTIONS FOR AVIATION FROM GATE BOARDING TO IN-FLIGHT, A BOEING/FAA/US CDC AND CANADIAN NRC COLLABORATION.
J. Armstrong¹, O.M. Ozcakir¹, L.L. Waite¹, T. Austin¹, C.A. Pepper¹, A. Tvaryanas², H. Uyhelji², S. Gearhart, US CDC, United States; P. Lebbin, Canadian NRC, Canada; I.S. Koolhof¹; ¹Boeing, Australia ;²FAA, United States
Global pandemics and seasonal epidemics have significant economic and health impacts on the aviation industry, travelling public, and to public health bodies. Despite the impact, the aviation industry currently lacks evidence-based knowledge of the mechanistic dynamics of infectious disease transmission in air travel and how multiple control measures fit together to prevent disease transmission. The work to be discussed was developed in response to the U.S. Department of Transportation National Aviation Preparedness Plan and is a collaboration between the FAA, US CDC, Boeing and the Canadian NRC. We have developed a systematic and holistic framework approach to examine disease transmission risk that is transferable to multiple pathogens and settings in the aviation system.rnrnThe framework provides a method and visualization by integrating a number of empirically driven mathematical models, including screening and testing, computational fluid dynamics, pathogen transmission; agent-based human movement and behaviour; and epidemiological models within the operational environment of commercial aircraft and airports. The framework defines the functionality required in each sub-model, and the required interfaces between models for data exchange. The integrated framework is then run using Monte Carlo methods to evaluate transmission risk and to optimise control strategies for a given pathogen. rnrnWe show that the framework can be used to simulate a variety of infectious agents and characterise control measures used in risk reduction and mitigation. The framework is able to be run in a systematic way that allows data to be transferred between the different sub-models and for each sub-model to be run independently and at the numerical fidelity required. rn