RAPID: Real-time updating of an agent-based model to inform COVID-19 mitigation strategies.

SARS-CoV-2病毒导致了这场百年不遇的大流行。由于目前还没有疫苗可用，非药物干预(NPI)是目前控制病毒的唯一途径。这些干预措施包括社会距离、学校关闭和就地避难，如果时机适当并广泛采用，可能是有效的。与此同时，NPI会造成严重的社会和经济混乱，这意味着必须尽可能节制地使用它们。要为何时采用NPI以及何时放松它们的决策提供信息，重要的是要了解这些行动可能产生的后果。这项研究将提高数学模型洞察这些后果的能力。展望过去的数据，研究人员将使用统计方法来估计关键的未知数，如以前或活跃感染的人数。展望未来，研究人员将使用美国各地社区的模拟模型来评估替代行动的后果。合并这些方法将利用各自的优势，导致对缓解美国新冠肺炎大流行的替代策略后果的更好预测。该项目将以美国SARS-CoV-2传播的地理上现实的、基于代理人的模型为特色。该模型的优势包括它详细地描述了美国特定县的人口密度、人口统计和流动模式，以及通过代理的行为修改直接实施NPI的能力。这些特点使得能够对SARS-CoV-2传播和非营利机构对其影响进行当地定制的预测。与此同时，基于代理的模型的计算需求对在大流行的快节奏环境中使用它们构成了挑战。为了克服这一挑战，研究人员将使用计算量要求较低的统计方法来估计在基于代理的模型中使用的投入，直到大流行中的某个给定点。然后，基于代理的模型将在有关使用NPI的替代场景下模拟从那时开始的情况。为了进一步防止计算需求成为产生及时结果的限制因素，研究人员将利用高性能计算资源对考虑随机性和参数不确定性的基于代理的模型进行批量模拟。研究结果将在项目过程中定期公开发布，研究人员将与利益相关者协调，以确保正在考虑的缓解情景在大流行进展时保持相关。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The SARS-CoV-2 virus is responsible for the most significant pandemic in a century. With a vaccine not yet available, non-pharmaceutical interventions (NPIs) offer the only way to control the virus at this time. Those interventions, which include social distancing, school closures, and sheltering in place, may be effective if timed appropriately and adopted widely. At the same time, NPIs cause serious social and economic disruption, meaning that they must be used as sparingly as possible. To inform decisions about when to adopt NPIs, and when to relax them, it is important to understand what the consequences of those actions might be. This research will advance the capability of mathematical models to provide insight into those consequences. Looking to past data, the researchers will use statistical approaches to estimate key unknowns, such as numbers of people previously or actively infected. Looking into the future, the researchers will use simulation modeling of communities across the United States to evaluate the consequences of alternative actions. Merging these approaches will capitalize on the strengths of each, resulting in improved projections of the consequences of alternative strategies for mitigating the COVID-19 pandemic in the United States.This project will feature a geographically realistic, agent-based model of SARS-CoV-2 transmission in the United States. Advantages of this model include its detailed portrayal of the density, demography, and movement patterns of people in specific counties across the United States, and its ability to directly implement NPIs through behavior modification of agents. These features enable locally tailored projections of SARS-CoV-2 transmission and impacts of NPIs thereon. At the same time, the computational demands of agent-based models pose a challenge to using them in the fast-paced context of a pandemic. To overcome that challenge, the researchers will use less computationally demanding statistical approaches to estimate inputs for use in the agent-based model up to a given point in the pandemic. The agent-based model will then simulate forward from that time under alternative scenarios about use of NPIs. To further safeguard against computational demands being a limiting factor for producing timely results, the researchers will make use of high-performance computing resources to perform batches of simulations of the agent-based model that account for stochasticity and parameter uncertainty. Results will be publicly disseminated on a regular basis over the course of the project, and the researchers will coordinate with stakeholders to ensure that mitigation scenarios under consideration remain relevant as the pandemic progresses.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.