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RAPID: Transmission and Immunology of COVID-19 in the Pandemic and Post-Pandemic Phase: Real-time Assessment of Social Distancing & Protective Immunity

RAPID：大流行和大流行后阶段 COVID-19 的传播和免疫学：社交距离的实时评估

基本信息

批准号：
2029421
负责人：
Micaela Martinez
金额：
$ 20万
依托单位：
Columbia University
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：
2020-05-01 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029421&HistoricalAwards=false
关键词：
RAPID Transmission Immunology COVID 19

项目摘要

In March 2020 the COVID-19 pandemic took hold in the United States and threatened the lives a livelihood of all Americans. At this time, the pandemic is being driven by person-to-person transmission of the coronavirus known as SARS-CoV-2. The U.S. government, both state and federal, responded to the pandemic by launching social distancing as an intervention, ordering schools and non-essential businesses to close throughout most of the country. Mathematical models are required in order to measure the transmission of COVID-19 in the U.S. This research focuses on building mathematical models that include data on social distancing to measure how effective the intervention is at slowing the disease. The researchers will also evaluate strategies for reopening schools and workplaces. Importantly, the researchers will measure the risk for a second pandemic wave by accounting for how the immune system reacts to the infection. This research is important because it will provide the U.S. government with models that it can use to choose among options for reopening cities. These models will also reveal the number of deaths averted by social distancing policies. In addition to the great societal benefit of this research, it also brings scientific advancement and broader impacts by demonstrating how novel datasets can be collected in real-time and models can be deployed during a public health emergency. This project provides professional development opportunities for an early career scientist. Transmission models will be used to explore creative ways for phased reopening of cities in order to minimize disease-induced mortality and overburdening of hospitals. The researchers will focus on the 62 counties in New York state, the current epicenter of the pandemic. The researchers will fit a city-level COVID-19 transmission model which accounts for social distancing quantified by Google traffic data, and ground-truthed by public transportation data, live-webcam streams, and Google trends indicating individuals are staying home. The researchers will explore transmission dynamics and hospitalization trajectories under different scenarios of adaptive immunity (e.g., long-lived sterilizing immunity, waning immunity, and immunity that reduces symptoms in subsequent infections). Models will be parameterized using data on testing, COVID-19 clinical cases, hospitalizations, and mortality via Maximum Likelihood by Iterated Particle Filtering (MIF). Modeling will be done in real time, requiring the statistical inference pipeline to be sufficiently nimble to account for the rapidly changing epidemiological situation. The researchers are interfacing with policy makers as part of CDC working groups on modeling COVID-19 and will actively share data through the NIH MIDAS coordination network.This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) ActThis 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.

2020年3月，COVID-19疫情在美国蔓延，威胁到所有美国人的生命和生计。目前，这场大流行是由被称为SARS-CoV-2的冠状病毒的人与人之间的传播所驱动的。美国政府，包括州政府和联邦政府，对疫情的反应是采取社交距离作为干预措施，下令全国大部分地区的学校和非必要企业关闭。为了衡量COVID-19在美国的传播情况，需要建立数学模型。这项研究的重点是建立数学模型，其中包括社交距离的数据，以衡量干预措施在减缓疾病方面的有效性。研究人员还将评估重新开放学校和工作场所的战略。重要的是，研究人员将通过计算免疫系统对感染的反应来衡量第二波大流行的风险。这项研究很重要，因为它将为美国政府提供模型，供其在重新开放城市的方案中做出选择。这些模型还将揭示社交距离政策避免的死亡人数。除了这项研究的巨大社会效益外，它还通过展示如何实时收集新的数据集以及如何在公共卫生紧急情况下部署模型来带来科学进步和更广泛的影响。该项目为早期职业科学家提供专业发展机会。传播模型将用于探索分阶段重新开放城市的创造性方法，以尽量减少疾病引起的死亡率和医院的过度负担。研究人员将把重点放在纽约州的62个县，这是目前流行病的中心。研究人员将拟合一个城市级的COVID-19传播模型，该模型考虑了由谷歌交通数据量化的社交距离，并通过公共交通数据、实时网络摄像头流和谷歌趋势（表明个人呆在家里）进行地面验证。研究人员将探索适应性免疫不同场景下的传播动力学和住院轨迹（例如，持久的杀菌免疫力、减弱的免疫力和减轻随后感染症状的免疫力）。模型将使用测试、COVID-19临床病例、住院和死亡率的数据通过迭代粒子滤波（MIF）的最大似然法进行参数化。建模将在真实的时间内完成，需要统计推断管道足够灵活，以说明快速变化的流行病学情况。研究人员作为CDC COVID-19建模工作组的一部分，正在与政策制定者进行交流，并将通过NIH MIDAS协调网络积极分享数据。该RAPID奖由环境生物学部传染病生态学和进化计划颁发，使用冠状病毒援助，救济，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。