COVID-19, Crowdedness, and CMC Dining: An Agent-Based Model Approach to Reducing the Spread of COVID-19

Abstract

. To make a COVID-safe return to in-person learning in the fall 2021 semester, Claremont McKenna College (CMC) created a new outdoor dining option to decrease the number of students inside the dining hall at one time: food trucks. However, crowding often occurs at both the inside and outside dining options. And so, we constructed an Agent-Based Model (ABM) to simulate the flow of students to the dining options over the lunch time hours. We use our ABM to investigate when over the lunch period crowding occurs and how often both or either option is crowded. Our analysis examines three different student behaviors namely, the ability to stick to an initial preference, the ability to sense crowding, and the ability to be influenced by other students. We find that the behavior that influences the level of crowding in the dining areas the most is having a strong preference for one of the dining areas. We also explore two different control measures that CMC could take to reduce crowding: adding another outdoor food option or increasing the amount of grab-and-go options. We find that adding another dining area is more effective in reducing crowding.