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.

该项目将开发一套关于 SARS-CoV-2（COVID-19 的病因）传播的数学模型。 2019-2020年全球新冠病毒大流行仍在持续，对国家内部和国家之间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特定地点之间传播风险如何随时间变化的方法，并且可以与其他数据源相结合，帮助确定主要传播途径以及成功减缓或停止传播的行动。传播。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强大的数据集。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，为 CDC 的决策和大流行应对措施提供信息，并将提供一种近乎实时评估输入风险变化的方法。此外，拟议的项目将有助于研究生和本科生的培养。随机空间模型提供了确定特定位置之间病原体传播随时间变化的风险的方法。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，其中包含每个位置随时间变化的情况，包括可靠的参数化方法。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大流行轨迹。这些模型还将有助于确定在控制本地传播时输入新病例风险最大的地区。这些目标将通过使用公开数据源并汇编有关干预措施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价值和更广泛的影响审查标准进行评估，被认为值得支持。