RAPID Collaborative proposal: Spatial dynamics of COVID-19

该项目将建立一套关于新冠肺炎病因SARS-CoV-2传播的数学模型。2019-2020年全球冠状病毒大流行正在持续，对国家内部和国家之间人员的正常流动构成了前所未有的挑战。这项研究开发的模型将提供确定传播风险在特定地点之间如何随时间变化的手段，并可与其他数据来源相结合，以帮助确定传播的主要途径以及成功减缓或阻止传播的行动。这些方法旨在提供具有有限数据的初始模型，同时允许在可用时集成更健壮的数据集。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，将为疾病预防控制中心的决策和应对大流行提供信息，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。随机空间模型提供了确定病原体在特定地点之间传播的时变风险的方法。该项目将开发这些模型的扩展，以便更有效地分析和预测新发疾病的空间传播。该项目将开发一个空间模型，其中包含每个位置的时变情况，包括可靠的参数化方法。它将使用这些模型来估计旅行限制和隔离对全球和美国境内传播的影响。通过仔细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了大流行的轨迹。这些模型还将有助于在当地传播得到控制的情况下确定输入新病例风险最大的地区。这些目标将通过利用公开的数据来源和汇编有关干预措施时间表的新信息来实现。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.