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Although the COVID-19 pandemic has mostly ended, there may be future situations (e.g. future pandemics) in which infectious disease spread on airplanes should be minimized. The COVID-19 pandemic led to social distancing as a means of enhancing passenger safety. Methods were developed to separate homogenous passengers from each other on airplanes and in other settings. This paper presents three greedy methods that assign passengers to airplane seats so that those passengers most likely to be susceptible to infectious diseases are separated from those passengers who are most likely to be infectious. Stochastic simulation results show that the performance of the proposed greedy methods provide much higher values for the average distance of separation between susceptible and infectious passengers when compared to a random seat assignment. The improvements in the two best of the three greedy methods range from 152% to 343% across the selected scenarios. In addition to considering passengers who are likely to be infectious and those who are likely to be susceptible to the disease, the methods consider those passengers who are likely to be both infectious and susceptible. By accounting for variations in individual passenger infectiousness and susceptibility to infection, we illustrate how disease spread may be reduced during future pandemics or similar health crises, thereby improving the safety and resiliency of air travel.

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Airplane Seating Assignment Greedy Algorithms that Separate Passengers Likely to be Susceptible to Infectious Disease from those Likely to be Infectious