RAPID Collaborative proposal: Spatial dynamics of COVID-19

该项目将开发一套关于新冠肺炎起因--SARS-CoV-2传播的数学模型。2019-2020年全球冠状病毒大流行正在进行中，对各国内部和国家之间的人员正常流动构成前所未有的挑战。这项研究开发的模型将提供确定传播风险如何随时间在特定地点之间变化的手段，并可与其他数据来源相结合，以帮助确定主要传播途径和成功减缓或阻止传播的行动。这些方法旨在为初始模型提供有限的数据，同时允许在获得更强大的数据集时进行集成。拟议的项目将产生直接适用于管理SARS-CoV-2地理传播的信息，将为疾控中心对大流行的决策和应对提供信息，并将提供一种接近实时评估输入风险变化的方法。此外，拟议的项目将有助于培养一名研究生和一名本科生。随机空间模型为确定病原体在特定地点之间传播的时变风险提供了手段。该项目将开发这些模型的扩展，以更有效地分析和预测新出现的疾病的空间传播。该项目将开发一个空间模型，将每个地点随时间变化的情况纳入其中，包括一种可靠的参数化方法。它将使用这些模型来估计旅行限制和隔离对全球和美国国内疫情传播的影响。通过密切研究疫情源头的传播，它将评估旅行模式和干预措施如何改变大流行的轨迹。当当地传播受到控制时，这些模型还将有助于确定新病例输入风险最大的地区。这些目标将通过使用公开可用的数据源和汇编关于干预时间表的新信息来实现。该奖项反映了NSF的法定使命，并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。

This project will develop a set of mathematical models of the spread of SARS-CoV-2, the cause of COVID-19. The 2019-2020 global corona virus pandemic is ongoing and poses an unprecedented challenge to the normal movement of people within and between countries. The models developed by this research will provide the means to determine how the risk of transmission varies over time between specific locations and can be combined with other data sources to help determine the primary routes of transmission and the actions that are successful in slowing or halting the spread. These methods are designed to provide initial models with limited data, while allowing for the integration of more robust datasets as they become available. The proposed project will produce information directly applicable to managing the geographic spread of SARS-CoV-2, will inform CDC decisions and responses to the pandemic, and will provide a way to assess changes to importation risk in close to real-time. In addition,the proposed project will contribute to the training of a graduate student and an undergraduate student. Stochastic spatial models provide the means for determining the time-varying risk of pathogen transmission between specific locations. This project will develop extensions of these models that will be more effective at analyzing and forecasting spatial spread of emerging disease. The project will develop a spatial model that incorporates time-varying cases in each location, including a reliable method for parameterization. It will use those models to estimate the effects of travel restrictions and quarantines on the spread globally and within the U.S. By closely studying the spread at the source of the outbreak it will assess how travel patterns and interventions altered the pandemic trajectory. The models will also assist in identifying areas at greatest risk of importation of new cases when local transmission is controlled. These aims will be accomplished by using publicly available data sources and compiling new information on the timeline of interventions.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.