RAPID: Dynamical Modeling of COVID-19

该项目将开发一套SARS-CoV-2传播的数学模型，SARS-CoV-2是COVID-19的病因。2019-2020年全球冠状病毒大流行仍在持续。关于这一流行病的发展轨迹和可能的遏制途径，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的有效性。在此过程中，这项工作将开发对于了解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兴传染病。拟议的工作将提供关键流行病学参数的估计，这些参数对于理解SARS-CoV-2作为公共卫生紧急事件的传播以及未来对其他新出现的人畜共患病的控制是必要的。该研究将为CDC的运营提供及时的COVID-19疫情信息，并有助于培训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与以前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。其次，它将估计和可视化COVID-19的流行病学参数，包括：流行曲线，基本繁殖数（R 0），病例发现率，潜伏期，传播率以及症状发作和隔离之间的滞后。第三，它将开发和参数化美国的空间模型，以帮助确定在多个空间尺度（如州、城市和医院）的人力、资金、物资等资源的最佳配置。第四，它将随机动态模型与病例通知数据相匹配，以得出有关封锁、宵禁和学校关闭等干预措施有效性的结论。这些目标将通过利用公共数据、众包数据和政府数据以及传输和空间传播的随机动态模型来实现。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.