标题： 从终点开始：一种计算政策探索框架，用于指导圣路易斯市有效的地理一致干预措施的实施 显示英文标题

标题： Start from the End: A Framework for Computational Policy Exploration to Inform Effective and Geospatially Consistent Interventions applied to COVID-19 in St. Louis

摘要： 数学模型是研究社会系统中传染病动态和应对策略的强大工具。 然而，由于其详细的实现和高昂的计算需求，实践者和利益相关者通常只能探索所有可能干预情景的一个小子集，这在准备疾病爆发时是一个严重的限制。 在本工作中，我们提出了一种参数探索框架，利用模拟器模型对高维参数空间进行不确定性感知的预测，并识别大量可行的应对策略。 我们将该框架应用于密苏里州圣路易斯市2021年12月至2022年2月期间的新冠状病毒“奥密克戎波”大规模基于代理的疾病模型案例研究。 我们识别出大量应对策略，这些策略原本预计可以显著减少疾病传播。 我们还识别出能够减少疾病传播的地理空间差异的政策干预措施，这对设计深思熟虑的应对策略具有额外的意义。 显示英文摘要

摘要： Mathematical models are a powerful tool to study infectious disease dynamics and intervention strategies against them in social systems. However, due to their detailed implementation and steep computational requirements, practitioners and stakeholders are typically only able to explore a small subset of all possible intervention scenarios, a severe limitation when preparing for disease outbreaks. In this work, we propose a parameter exploration framework utilizing emulator models to make uncertainty-aware predictions of high-dimensional parameter spaces and identify large numbers of feasible response strategies. We apply our framework to a case study of a large-scale agent-based disease model of the COVID-19 ``Omicron wave'' in St. Louis, Missouri that took place from December 2021 to February 2022. We identify large numbers of response strategies that would have been estimated to have reduced disease spread by a substantial amount. We also identify policy interventions that would have been able to reduce the geospatial variation in disease spread, which has additional implications for designing thoughtful response strategies.