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

COVID-19的传播对人类健康和全球经济产生广泛影响。这个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轴。然后，这些数据将被用来模拟病毒如何通过各种网络依赖的流行病模型在社区之间传播。最后，将利用收集到的数据和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传播方面的有效性，同时考虑经济影响。了解社区之间的交通网络如何成为病毒的传播者，以及地方和国家政府为限制或阻止区域内和区域间的旅行而采取的控制行动如何减缓病毒的传播，将为制定COVID-19大流行以及未来其他可能爆发的缓解策略提供框架。这些战略将限制人的生命损失，减少病毒的经济影响。这项工作的结果所开发的方法也将有利于在未来对抗随后的疫情。该项目将应用网络建模技术，以了解交通网络上的不同控制措施如何影响社区之间的病毒传播。在此获得的理解将告知决策者在这次和未来的疫情期间，在不同情况下最好使用哪些与运输有关的缓解策略，以及在疫情爆发的什么时候使用它们，以尽量减少病毒的传播以及经济影响。该研究将借鉴并有助于统计数据分析，数学建模和流行过程分析，数学规划，网络分析和控制理论的广泛和基本成果。该奖项反映了NSF的法定使命，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。

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.