A Low-Fidelity Stochastic Model of Viral Spread in Aircraft to Assess Risk Mitigation Strategies

Abstract

The purpose of this work is to develop a computationally efficient model of viral spread that can be utilized to better understand the influences of stochastic factors on a large-scale system – such as the air traffic network. A particle-based model of passengers and seats aboard a single-cabin 737-800 is developed for use as a demonstration of the concept on tracking the propagation of a virus through the aircraft's passenger compartment over multiple flights. The model is sufficiently computationally efficient to be viable for Monte Carlo simulation to capture various stochastic effects, such as number of passengers, number of initially sick passengers, seating locations of passengers, and baseline health of each passenger. The computational tool is then exercised in demonstration for assessing risk mitigation of intervention strategies, such as passenger-driven cleaning of seating environments and elimination of middle seating.