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.

翻译：本文报告了一项关于高分辨率智能体建模与仿真在大学校园疫情应对规划中应用的案例研究。2020年夏季，我们承担了一项COVID-19疫情应对项目，旨在为宾汉姆顿大学全校人群创建详细的行为仿真模型。我们将该问题概念化为多层交通网络上的智能体迁移过程，其中每一层代表一种不同的交通方式。由于缺乏关于校园人群行为的直接数据，我们尽可能收集间接信息来制定智能体的行为规则。假设每个智能体在每个交通层内沿两个地点之间的最短路径移动，并在停车场或公交站切换交通层，同时辅以若干其他行为假设。利用该模型，我们对典型工作日全校超过25,000名智能体的人群行为进行了仿真。我们测量了每个空间位置上的密切社交接触频率，并识别出校园内需要重大行为干预的若干繁忙地点和走廊。此外，针对不同人口密度的系统仿真揭示，人口密度降低的效果是非线性的，将人口密度降低至40-45%将是抑制校园内疾病传播的最佳且足够的措施。这些结果已向大学管理部门汇报，并应用于疫情应对规划中，最终取得了成功成效。