Computational models help decision makers understand epidemic dynamics to optimize public health interventions. Agent-based simulation of disease spread in synthetic populations allows us to compare and contrast different effects across identical populations or to investigate the effect of interventions keeping every other factor constant between ``digital twins''. FRED (A Framework for Reconstructing Epidemiological Dynamics) is an agent-based modeling system with a geo-spatial perspective using a synthetic population that is constructed based on the U.S. census data. In this paper, we show how Gaussian process regression can be used on FRED-synthesized data to infer the differing spatial dispersion of the epidemic dynamics for two disease conditions that start from the same initial conditions and spread among identical populations. Our results showcase the utility of agent-based simulation frameworks such as FRED for inferring differences between conditions where controlling for all confounding factors for such comparisons is next to impossible without synthetic data.

翻译：计算模型帮助决策者理解疫情动力学，以优化公共卫生干预措施。基于合成人群的疾病传播代理模拟使我们能够比较和对比相同人群中的不同效应，或研究在“数字孪生”之间保持其他所有因素恒定时的干预效果。FRED（流行病动力学重构框架）是一个基于代理的建模系统，采用地理空间视角，利用基于美国人口普查数据构建的合成人群。本文展示了如何使用高斯过程回归对FRED合成数据进行分析，以推断从相同初始条件开始并在相同人群中传播的两种疾病状态的疫情动力学的空间离散差异。我们的结果展示了FRED等代理模拟框架在推断条件差异方面的实用性——在缺乏合成数据的情况下，几乎无法控制所有混杂因素进行此类比较。