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流行病动力学，从而优化公共卫生干预措施。基于智能体的人工合成人群疾病传播模拟，使我们能够在相同人群中比较和对比不同效应，或在“数字孪生”之间保持其他所有因素恒定的情况下研究干预措施的效果。FRED（流行病动力学重构框架）是一个基于智能体的建模系统，采用地理空间视角，并使用基于美国人口普查数据构建的人工合成人群。在本文中，我们展示了如何利用高斯过程回归对FRED合成数据进行分析，以推断两种疾病条件下流行病动力学的空间分散性差异，这两种疾病都从相同的初始条件开始，并在相同人群中传播。我们的结果展示了像FRED这样的基于智能体模拟框架的实用性，它能够推断不同条件之间的差异，而在没有合成数据的情况下，控制比较中所有混杂因素几乎是不可能的。