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

RAPID：实时更新基于代理的模型，以告知 COVID-19 缓解策略。

• 批准号: 2027718
• 负责人: Alex Perkins
• 金额: $ 19.99万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027718&HistoricalAwards=false
• 关键词: RAPID; Real; time; updating; agent

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.

SARS-CoV-2 病毒造成了一个世纪以来最严重的大流行。由于疫苗尚未上市，非药物干预措施 (NPI) 是目前控制病毒的唯一方法。这些干预措施，包括保持社交距离、关闭学校和就地避难，如果时机适当并得到广泛采用，可能会有效。与此同时，非营利机构造成了严重的社会和经济混乱，这意味着必须尽可能谨慎地使用它们。为了告知何时采用 NPI 以及何时放宽 NPI，了解这些行动可能产生的后果非常重要。这项研究将提高数学模型的能力，以深入了解这些后果。研究人员将根据过去的数据，使用统计方法来估计关键的未知因素，例如先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，从而更好地预测减轻美国 COVID-19 大流行的替代策略的后果。该项目将采用一个基于地理因素的 SARS-CoV-2 在美国传播模型。该模型的优点包括详细描述了美国特定县的人口密度、人口统计和流动模式，以及通过代理行为修正直接实施 NPI 的能力。这些特征使得能够对 SARS-CoV-2 传播和 NPI 对其影响进行本地定制的预测。与此同时，基于代理的模型的计算需求对在快节奏的大流行背景下使用它们提出了挑战。为了克服这一挑战，研究人员将使用计算要求较低的统计方法来估计在大流行的给定点之前用于基于代理的模型的输入。然后，基于代理的模型将从那时起在有关使用 NPI 的替代场景下进行模拟。为了进一步防止计算需求成为及时产生结果的限制因素，研究人员将利用高性能计算资源对基于代理的模型进行批量模拟，以解释随机性和参数不确定性。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方案随着大流行的进展仍然具有相关性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Estimating unobserved SARS-CoV-2 infections in the United States

• DOI: 10.1073/pnas.2005476117
• 发表时间: 2020-09-08
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Perkins, T. Alex;Cavany, Sean M.;Poterek, Marya
• 通讯作者: Poterek, Marya