RAPID: Dynamical Modeling of COVID-19

RAPID：COVID-19 的动态建模

• 批准号: 2027786
• 负责人: John Drake
• 金额: $ 19.99万
• 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 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027786&HistoricalAwards=false
• 关键词: RAPID; Dynamical; Modeling; COVID; 19

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 coronavirus pandemic is ongoing. Much remains unknown about the epidemic trajectory and potential paths to containment. The researchers will map the spatial spread of the virus, estimate key parameters related to transmission, compile clinical and epidemiological information, and assess the effectiveness of public health interventions on containment. In doing so the work will develop concepts and mathematical theory essential for the understanding of the spread of COVID-19 with possible applications to other emerging infectious diseases. The proposed work will provide estimates of key epidemiological parameters necessary for understanding the spread of SARS-CoV-2 as a public health emergency, in particular, and the future control of other emerging zoonosis. This research will provide timely information on the COVID-19 epidemic immediately useful to the operations of the CDC, as well as contribute to the training of two graduate students and a postdoc.The specific aims of this project are, first, to contextualize COVID-19 in comparison to previous outbreaks of infectious respiratory diseases and estimate infectiousness, incubation period, transmissibility, case severity, and case fatality rate. Second, it will estimate and visualize epidemiological parameters for COVID-19, including: the epidemic curve, the basic reproduction number (R0), the case detection rate, the incubation period, transmissibility, and the lag between symptom onset and isolation. Third, it will develop and parameterize a U.S. spatial model to help determine the optimal allocation of resources such as personnel, funding, supplies at multiple spatial scales such as states, cities, and hospitals. Fourth, it will fit stochastic dynamical models to case notification data to draw conclusions about the effectiveness of interventions such as lockdowns, curfews, and school closures. These aims will be accomplished by leveraging public, crowd-sourced, and government data with stochastic dynamical models for transmission and spatial spread.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。关于疫情的传播轨迹和潜在的遏制途径，仍有许多未知之处。研究人员将绘制病毒的空间传播地图，估计与传播相关的关键参数，汇编临床和流行病学信息，并评估公共卫生干预措施对遏制措施的有效性。在这样做的过程中，这项工作将发展出对理解新冠肺炎的传播至关重要的概念和数学理论，并可能应用于其他新出现的传染病。拟议的工作将提供必要的关键流行病学参数的估计，以了解SARS-CoV-2作为公共卫生紧急情况的传播，特别是未来对其他新出现的人畜共患病的控制。这项研究将提供有关新冠肺炎疫情的及时信息，对疾控中心的运作立即有用，并有助于两名研究生和一名博士后的培训。该项目的具体目标是，首先，将新冠肺炎与以前爆发的传染性呼吸道疾病进行比较，并估计传染性、潜伏期、传播性、病例严重性和病死率。其次，它将估计和可视化新冠肺炎的流行病学参数，包括：疫情曲线、基本繁殖数(R0)、病例发现率、潜伏期、传播力以及症状出现和隔离之间的滞后。第三，它将开发一个美国空间模型并将其参数化，以帮助确定人员、资金、物资等资源在州、城市和医院等多个空间尺度上的最佳配置。第四，它将把随机动态模型与病例通报数据相匹配，以得出关于封锁、宵禁和学校关闭等干预措施的有效性的结论。这些目标将通过利用公共的、众包的和政府的数据以及用于传输和空间传播的随机动力学模型来实现。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。

项目成果

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

• DOI: 10.1073/pnas.2113561119
• 发表时间: 2022-04-12
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1