RAPID: Inference, Forecasting, and Intervention Modeling of COVID-19

RAPID：COVID-19 的推理、预测和干预建模

基本信息

批准号：
2027369
负责人：
Jeffrey Shaman
金额：
$ 19.86万
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：
2020-04-15 至 2021-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027369&HistoricalAwards=false
关键词：
RAPID Inference Forecasting Intervention Modeling

项目摘要

A novel coronavirus emerged in late 2019 and by the end of March 2020 had spread to more than 170 countries, causing more than 750,000 confirmed cases of COVID-19 disease and over 4000 deaths. There is an urgent need to better understand this rapidly evolving crisis to predict and mitigate its effects. The focus of this project is to apply mathematical and statistical models to: 1) estimate critical characteristics of the virus and its transmission; 2) forecast new COVID-19 outcomes; and 3) estimate the potential effectiveness of non-pharmaceutical (e.g. school closures, travel restrictions) interventions. Projections of COVID-19 cases, hospital bed demand and ventilator demand, will be communicated to the public, public health agencies and government officials as they are developed in order to support real-time policy decision making. Inference, forecasting, and intervention modeling of the COVID-19 outbreak in the US is needed to improve understanding of SARS-CoV2 transmission dynamics and to support COVID-19 response and intervention efforts. For this project, observations of reported infections in the US, in conjunction with commuting data, a networked dynamic metapopulation model and Bayesian inference, will be used to infer critical and evolving characteristics associated with SARS-CoV2 spread in the US, including the fraction of undocumented infections and their contagiousness. Using these same model-inference methods, ensemble projections of future COVID-19 incidence in the US will be developed and generated. Finally, models will be developed to simulate and study the effects of non-pharmaceutical interventions, including school closure, isolation, quarantine and travel restrictions, on COVID-19 incidence. Project findings will be communicated in real time to public health and government officials. This award is co-funded with the Ecology and Evolution of Infectious Diseases program (Division of Environmental Biology), the Applied Mathematics program (Division of Mathematical Sciences), and the Office of Multidisciplinary Activities (OMA) program.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.

一种新颖的冠状病毒在2019年底出现，到2020年3月底，已蔓延到170多个国家，造成了750,000多例确认的COVID-19-COVID-19疾病和4000多人死亡。迫切需要更好地了解这种快速发展的危机，以预测和减轻其影响。该项目的重点是将数学和统计模型应用于：1）估计病毒及其传播的关键特征； 2）预测新的Covid-19结果； 3）估计非药物（例如学校关闭，旅行限制）干预措施的潜在有效性。为了支持实时的政策决策，将向公共，公共卫生机构和政府官员传达对COVID-19案件，医院床需求和呼吸机需求的预测，医院床的需求和呼吸机需求。需要推断，预测和干预模型在美国进行COVID-19爆发，以提高对SARS-COV2传输动力学的了解并支持COVID-19-19S的响应和干预工作。对于该项目，将观察到美国报告的感染，并结合通勤数据，一个网络动态化型模型和贝叶斯推断，将用于推断与美国SARS-COV2在美国相关的关键和不断发展的特征，包括无备文化感染的馏分及其感染性。使用这些相同的模型推断方法，将开发和生成美国未来Covid-19发病率的集合预测。最后，将开发模型来模拟和研究非药物干预措施的影响，包括学校关闭，隔离，隔离和旅行限制对COVID-19的发病率。项目发现将实时传达给公共卫生和政府官员。该奖项与传染病计划（环境生物学系），应用数学计划（数学科学系）和多学科活动（OMA）计划的办公室（OMA）计划共同资助。该奖项反映了NSF的法定任务，并通过评估了基金会的智力效果，并已被视为支持者的法定任务。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Mask-wearing and control of SARS-CoV-2 transmission in the USA: a cross-sectional study.

DOI：
10.1016/s2589-7500(20)30293-4
发表时间：
2021-03
期刊：
The Lancet. Digital health
影响因子：
0
作者：
Rader B;White LF;Burns MR;Chen J;Brilliant J;Cohen J;Shaman J;Brilliant L;Kraemer MUG;Hawkins JB;Scarpino SV;Astley CM;Brownstein JS
通讯作者：
Brownstein JS

Associations Between Built Environment, Neighborhood Socioeconomic Status, and SARS-CoV-2 Infection Among Pregnant Women in New York City