  This paper reports a case study of an application of high-resolution
agent-based modeling and simulation to pandemic response planning on a
university campus. In the summer of 2020, we were tasked with a COVID-19
pandemic response project to create a detailed behavioral simulation model of
the entire campus population at Binghamton University. We conceptualized this
problem as an agent migration process on a multilayer transportation network,
in which each layer represented a different transportation mode. As no direct
data were available about people's behaviors on campus, we collected as much
indirect information as possible to inform the agents' behavioral rules. Each
agent was assumed to move along the shortest path between two locations within
each transportation layer and switch layers at a parking lot or a bus stop,
along with several other behavioral assumptions. Using this model, we conducted
simulations of the whole campus population behaviors on a typical weekday,
involving more than 25,000 agents. We measured the frequency of close social
contacts at each spatial location and identified several busy locations and
corridors on campus that needed substantial behavioral intervention. Moreover,
systematic simulations with varying population density revealed that the effect
of population density reduction was nonlinear, and that reducing the population
density to 40-45% would be optimal and sufficient to suppress disease spreading
on campus. These results were reported to the university administration and
utilized in the pandemic response planning, which led to successful outcomes.
