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：本文报告了一项应用高分辨率基于智能体的建模与仿真技术进行大学校园疫情应对规划的案例研究。2020年夏季，我们承担了一项COVID-19疫情应对项目，旨在为宾厄姆顿大学构建覆盖全校人口的详细行为仿真模型。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，其中每一层代表不同的交通模式。由于无法直接获取校园内人员行为数据，我们收集了尽可能多的间接信息来构建智能体的行为规则。每个智能体被设定为在交通层内沿最短路径移动，并在停车场或公交站点进行层间转换，同时遵循若干其他行为假设。利用该模型，我们对典型工作日的全校人口行为进行了仿真，涉及超过25,000个智能体。我们测量了各空间位置的密切社交接触频率，识别出校园内若干需要重点行为干预的繁忙区域与通道。此外，通过系统性地改变人口密度进行仿真，我们发现降低人口密度的影响是非线性的，将人口密度降至40-45%能最优且有效地抑制校园内的疾病传播。这些结果已提交大学管理部门并应用于疫情应对规划，最终取得了良好成效。