RAPID: Dynamical Modeling 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进行中。关于疫情的发展轨迹和潜在的遏制途径，还有很多未知之处。研究人员将绘制病毒的空间传播图，估计与传播有关的关键参数，汇编临床和流行病学信息，并评估公共卫生干预措施的有效性。在此过程中，这项工作将发展对理解COVID-19传播至关重要的概念和数学理论，并可能应用于其他新发传染病。拟议的工作将提供必要的关键流行病学参数估计，以了解SARS-CoV-2作为突发公共卫生事件的传播，特别是未来控制其他新出现的人畜共患病。这项研究将为疾控中心的工作提供及时的疫情信息，并有助于培养两名研究生和一名博士后。该项目的具体目标是：首先，将COVID-19与以往的传染性呼吸道疾病爆发进行比较，并估计传染性、潜伏期、传播性、病例严重程度和病死率。二是对COVID-19的流行病学参数进行估计和可视化，包括：流行曲线、基本繁殖数（R0）、病例检出率、潜伏期、传播力、症状出现与隔离之间的滞后时间。第三，它将开发和参数化一个美国空间模型，以帮助确定人员、资金、物资等资源在多个空间尺度（如州、城市和医院）的最佳配置。第四，将随机动态模型与病例通报数据拟合，得出关于封锁、宵禁和学校停课等干预措施有效性的结论。这些目标将通过利用公共、众包和政府数据来实现，这些数据具有传输和空间传播的随机动态模型。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 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.