RAPID Collaborative proposal: Spatial dynamics of COVID-19

RAPID 合作提案：COVID-19 的空间动态

• 批准号: 2028136
• 负责人: John Drake
• 金额: $ 7.3万
• 依托单位: University of Georgia Research Foundation Inc
• 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: 2020-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028136&HistoricalAwards=false
• 关键词: RAPID; Collaborative; proposal; Spatial; dynamics

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.

该项目将开发一组SARS-COV-2传播的数学模型，即Covid-19的原因。 2019 - 2020年全球电晕病毒大流行正在进行中，并对国家内部和国家之间的正常运动构成了前所未有的挑战。这项研究开发的模型将提供一些方法，以确定特定位置之间的传输风险如何随着时间的流逝而变化，并可以与其他数据源结合在一起，以帮助确定主要传输途径以及成功减缓或停止差异的动作。这些方法旨在为初始模型提供有限的数据，同时允许在可用时集成更强大的数据集。拟议的项目将直接提供适用于管理SARS-COV-2的地理扩散的信息，将为CDC的决策和对大流行的响应提供信息，并将提供一种评估接近实时的进口风险变化的方法。此外，拟议的项目将有助于培训研究生和一名本科生。随机空间模型提供了确定特定位置之间病原体传播的时间变化风险的手段。该项目将开发这些模型的扩展，这些模型将在分析和预测新兴疾病的空间传播方面更有效。该项目将开发一个空间模型，该模型在每个位置中都包含了时变案例，包括一种可靠的参数化方法。它将利用这些模型来估计旅行限制和隔离对全球范围以及美国境内扩散的影响，通过密切研究疫情来源的传播，它将评估旅行模式和干预措施如何改变大流行轨迹。这些模型还将有助于确定在控制本地传输时最大的进口新案例风险的区域。这些目标将通过使用公开可用的数据源并在干预时间表上编写新信息来实现。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的审查标准，认为值得通过评估来获得支持。