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：基于智能体的仿真作为分析复杂系统的有力工具，在整合地理要素时会因计算需求增加而面临挑战。本研究引入一系列“细胞中智能体”的基于智能体模型，利用地理特征和Safegraph提供的真实移动数据，模拟城市中COVID的传播。我们突破传统的聚合传播概率方法，基于物理学传播研究，聚焦于直接的人与人接触概率。通过创新策略应对计算复杂性：将称为“元智能体”的智能体与城市镶嵌中的特定家庭细胞关联。我们探究不同镶嵌类型与智能体密度，发现基于特定街道网络位置的Voronoi图镶嵌相比人口普查区块组镶嵌更能保持动力学特征。此外，结合Voronoi图与人口普查区块组的混合镶嵌方法在较少元智能体条件下仍能准确表征城市动力学。研究涵盖美国不同规模城市，揭示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进行基准对比，重点关注运行时间与缩减智能体数量的影响。关键优化包括元智能体使用、先进镶嵌方法及并行计算技术。本研究结论为需要地理特异性和高计算效率的基于智能体建模场景提供了理论贡献。