Agent-based simulation, a powerful tool for analyzing complex systems, faces challenges when integrating geographic elements due to increased computational demands. This study introduces a series of 'agent-in-the-cell' Agent-Based Models to simulate COVID spread in a city, utilizing geographical features and real-world mobility data from Safegraph. We depart from traditional aggregated transmission probabilities, focusing on direct person-to-person contact probabilities, informed by physics-based transmission studies. Our approach addresses computational complexities through innovative strategies. Agents, termed 'meta-agents', are linked to specific home cells in a city's tessellation. We explore various tessellations and agent densities, finding that Voronoi Diagram tessellations, based on specific street network locations, outperform Census Block Group tessellations in preserving dynamics. Additionally, a hybrid tessellation combining Voronoi Diagrams and Census Block Groups proves effective with fewer meta-agents, maintaining an accurate representation of city dynamics. Our analysis covers diverse city sizes in the U.S., offering insights into agent count reduction effects, sensitivity metrics, and city-specific factors. We benchmark our model against an existing ABM, focusing on runtime and reduced agent count implications. Key optimizations include meta-agent usage, advanced tessellation methods, and parallelization techniques. This study's findings contribute to the field of agent-based modeling, especially in scenarios requiring geographic specificity and high computational efficiency.

翻译：智能体仿真作为分析复杂系统的有力工具，在整合地理要素时因计算需求增加而面临挑战。本研究引入一系列"细胞内智能体"智能体模型来模拟城市中的新冠病毒传播，该模型利用地理特征和来自Safegraph的真实世界移动数据。我们摒弃传统的聚合传播概率方法，转而基于物理学传播研究，聚焦于直接的人与人接触概率。通过创新策略解决计算复杂性问题：将称为"元智能体"的智能体关联至城市镶嵌中的特定家庭细胞。通过探索不同镶嵌模式与智能体密度，我们发现基于特定街道网络位置的Voronoi图镶嵌在保持动力学特性方面优于人口普查街区组镶嵌。此外，结合Voronoi图与人口普查街区组的混合镶嵌方案在使用较少元智能体的情况下仍能保持城市动态的准确表征。本研究涵盖美国不同规模城市，分析了智能体数量缩减效应、敏感性指标及城市特异性因素。我们将模型与现有ABM进行基准测试，重点关注运行时间与智能体数量缩减的影响。核心优化方法包括元智能体技术、先进镶嵌策略及并行化技术。本研究成果对需要地理特异性和高计算效率的智能体建模场景具有重要参考价值。