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RAPID: Collaborative Research: Using Data to Understand the Effects of Transportation on the Spread of COVID-19 as a Propagator and a Control Mechanism

RAPID：协作研究：利用数据了解交通作为传播者和控制机制对 COVID-19 传播的影响

基本信息

批准号：
2028946
负责人：
Raphael Stern
金额：
$ 7.5万
依托单位：
University of Minnesota-Twin Cities
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：
2020-07-15 至 2021-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028946&HistoricalAwards=false
关键词：
RAPID Collaborative Research Using Data

项目摘要

The spread of COVID-19 has broad implications both for human health and economies around the world. This Smart and Connected Communities project will monitor the spread of COVID-19 by collecting real-time information on active COVID-19 cases, understand how transportation has driven the spread of the virus, and quantify how travel restrictions have limited the spread of the virus. The data collection will gather and store real-time information on the spread of COVID-19 and a timeline of travel restrictions for three sets of communities. This data will then be employed to model how the virus propagates between communities via transportation using various network-dependent epidemic models. Finally, using the collected data and the calibrated epidemic models, analysis will be conducted to understand how effective the different modifications of the transportation network structure, such as travel restrictions in each set of communities, are at slowing the spread of COVID-19, while factoring in the economic effects. Understanding how the transportation network between communities acts as a propagator of the virus, and how control actions taken by local and national governments to limit or block travel within and between regions slow the spread of the virus will provide the framework for the development of mitigation strategies for the COVID-19 pandemic, as well as other possible outbreaks in the future. These strategies will limit the loss of human life and reduce the economic impacts of the virus. The methods developed as a result of this work will also be beneficial in the future for battling subsequent outbreaks.This project will apply network modeling techniques to understand how different control actions on the transportation network influence the spread of the virus between communities. The understanding gained herein will inform decision makers during this and future outbreaks as to which transportation-related mitigation strategies are best to use in different situations and at what point in the outbreak to use them in order to minimize both the spread of virus as well as the economic impact. The research will draw on and contribute to wide-ranging and fundamental results in statistical data analysis, mathematical modeling and analysis of epidemic processes, mathematical programming, network analysis, and control theory. The resulting study of problems will contribute to advancement of mathematical modeling and analysis of infectious diseases, and mitigation optimization algorithms and heuristics.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.

COVID-19 的传播对世界各地的人类健康和经济产生广泛影响。该智能互联社区项目将通过收集活跃的 COVID-19 病例的实时信息来监测 COVID-19 的传播，了解交通如何推动病毒的传播，并量化旅行限制如何限制病毒的传播。数据收集将收集和存储有关 COVID-19 传播的实时信息以及三组社区的旅行限制时间表。然后，这些数据将用于使用各种依赖于网络的流行病模型来模拟病毒如何通过运输在社区之间传播。最后，利用收集到的数据和经过校准的流行病模型进行分析，以了解交通网络结构的不同修改（例如每组社区的旅行限制）在减缓 COVID-19 传播方面的效果，同时考虑到经济影响。了解社区之间的交通网络如何充当病毒的传播者，以及地方和国家政府为限制或阻止区域内和区域间旅行而采取的控制行动如何减缓病毒的传播，将为制定针对 COVID-19 大流行以及未来可能爆发的其他疫情的缓解策略提供框架。这些策略将限制人员生命损失并减少病毒的经济影响。这项工作开发的方法也将有益于将来对抗随后的疫情爆发。该项目将应用网络建模技术来了解交通网络上的不同控制措施如何影响病毒在社区之间的传播。本文获得的理解将帮助决策者在这次和未来的疫情爆发期间了解在不同情况下最好使用哪些与交通相关的缓解策略，以及在疫情爆发的什么时候使用这些策略，以最大限度地减少病毒的传播和经济影响。该研究将借鉴并贡献统计数据分析、流行病过程数学建模和分析、数学规划、网络分析和控制理论方面的广泛基础成果。由此产生的问题研究将有助于传染病的数学建模和分析以及缓解优化算法和启发式的进步。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The Effect of Population Flow on Epidemic Spread: Analysis and Control

DOI：
10.1109/cdc45484.2021.9683081
发表时间：
2021-04
期刊：
2021 60th IEEE Conference on Decision and Control (CDC)
影响因子：
0
作者：
Brooks A. Butler;Ciyuan Zhang;I. Walter;N. Nair;Raphael E. Stern;Philip E. Par'e
通讯作者：
Brooks A. Butler;Ciyuan Zhang;I. Walter;N. Nair;Raphael E. Stern;Philip E. Par'e

Quantifying the Traffic Impacts of the COVID-19 Shutdown