High-Resolution Agent-Based Modeling of Campus Population Behaviors for Pandemic Response Planning
<|reference_start|>High-Resolution Agent-Based Modeling of Campus Population Behaviors for Pandemic Response Planning: 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.<|reference_end|>
@article{sayama2024high-resolution,
    title={High-Resolution Agent-Based Modeling of Campus Population Behaviors for
  Pandemic Response Planning},
    author={Hiroki Sayama and Shun Cao},
    journal={arXiv preprint arXiv:2405.11414},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11414},
    primaryClass={cs.CY cs.CE cs.MA physics.soc-ph}
}