Collaborative Research: A comprehensive approach to modeling, learning, analysis and control of epidemic processes over time-varying and multi-layer networks

协作研究：时变多层网络上的流行病过程建模、学习、分析和控制的综合方法

• 批准号: 2032258
• 负责人: Philip Pare
• 金额: $ 20万
• 依托单位: Purdue 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: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032258&HistoricalAwards=false
• 关键词: Collaborative; Research; comprehensive; approach; modeling

Numerous natural and engineered systems consist of underlying networked components over which dynamical processes evolve. Examples include the spread of infectious disease processes over human contact and international travel networks, the propagation of peak traffic phenomenon over the transportation infrastructure, the spread of viruses or worms over computer networks, and sharing and re-sharing of posted articles, tweets, or rumors over social media platforms. Engineered systems are increasingly interconnected over various levels of both local and large-scale networks. Developing a stronger understanding at fundamental and analytical levels of how viral processes evolve across different network structures, the rates at which they spread, how the occurrence of multiple viral types and multiple network layers affect the spread process dynamics, and how these processes can be suppressed and/or mitigated by employing deliberate control policies will greatly impact the health, safety and security of a vast variety of systems around the globe. The spread of COVID-19 has clearly had broad implications for the health of people on all six inhabited continents as well as the world's economy. The research proposed herein will substantially enhance our understanding of epidemics such as COVID-19 and lead to general policy guidelines that will help limit the loss of human life and reduce the economic impacts of the virus. The methods to be developed in this project will be beneficial for battling subsequent epidemic outbreaks, a second wave of COVID-19, and on a broader scale general viral process. Throughout this project the PIs will build on their past experiences to make every effort towards recruiting and mentoring students from under-represented groups, and will establish outreach efforts by including undergraduate and local high school student researchers.Although the dynamics of epidemic processes over networks have been extensively studied for the past 10-15 years, past work has been focused largely on SIS and SIR processes spreading over static networks. In the proposed project, our focus will be on modeling, analysis and control of dynamic epidemic processes over large and possibly time-varying networks, comprised of multiple layers at multiple scales. We will specifically consider data-informed modeling and analysis of SAIRS (susceptible-asymptomatic-infected-recovered-susceptible) processes over time-varying networks; this work will include stability and equilibria analysis of the nonlinear dynamics of networked epidemic process models, network structure identification, estimation of parameters and structure from imperfect and non-random data, and development of realizable control strategies from the agent level to societal levels. The research proposed will draw on and contribute to wide-ranging foundational results in mathematical modeling and analysis of infectious diseases, time-varying nonlinear and linear analysis methods, optimization and control-theoretic policy formulation, network inference and analysis, sequential sampling strategies with stochastic sample constraints, and mean-field games over networks.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.

许多自然和工程系统由底层的网络组件组成，动态过程在这些组件上演化。例如，传染病过程通过人类接触和国际旅行网络传播，高峰交通现象在交通基础设施上传播，病毒或蠕虫在计算机网络上传播，以及在社交媒体平台上分享和再分享张贴的文章、推文或谣言。工程系统在不同级别的本地和大规模网络上的互连程度越来越高。在基础和分析层面上更深入地了解病毒过程如何在不同的网络结构中演变，它们的传播速度，多种病毒类型和多个网络层的出现如何影响传播过程的动态，以及如何通过采用有意的控制策略来抑制和/或缓解这些过程，将极大地影响全球各种系统的健康、安全和安保。新冠肺炎的传播显然对六大洲人民的健康以及世界经济产生了广泛影响。这里提出的研究将大大增强我们对新冠肺炎等流行病的了解，并产生有助于限制人类生命损失和减少该病毒的经济影响的一般政策指导方针。该项目开发的方法将有利于抗击后续的疫情暴发、第二波新冠肺炎，以及更广泛的一般病毒过程。在整个项目中，PIS将以他们过去的经验为基础，尽一切努力从代表性不足的群体中招收和指导学生，并将通过包括本科生和当地高中学生研究人员来建立外展努力。尽管在过去10-15年里，人们已经广泛研究了网络上的流行病过程的动态，但过去的工作主要集中在静态网络上的SIS和SIR过程上。在拟议的项目中，我们的重点将放在大型且可能是时变的网络上的动态流行病过程的建模、分析和控制上，该网络由多个级别的多层组成。我们将具体考虑时变网络上SAIRS(susceptible-asymptomatic-infected-recovered-susceptible)过程的数据信息建模和分析；这项工作将包括网络流行病过程模型的非线性动力学的稳定性和平衡性分析，网络结构识别，从不完美和非随机数据估计参数和结构，以及从主体级别到社会级别的可实现控制策略的发展。建议的研究将借鉴和促进传染病的数学建模和分析、时变的非线性和线性分析方法、优化和控制理论政策制定、网络推理和分析、随机样本约束的序贯抽样策略以及网络上的平均场游戏等广泛的基础性成果。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Parameter Estimation in Epidemic Spread Networks Using Limited Measurements

使用有限测量的流行病传播网络中的参数估计

• DOI: 10.1137/20m1377801
• 发表时间: 2021
• 期刊: SIAM Journal on Control and Optimization
• 影响因子: 2.2
• 作者: Ye, Lintao;Paré, Philip E.;Sundaram, Shreyas
• 通讯作者: Sundaram, Shreyas

Change time estimation uncertainty in nonlinear dynamical systems with applications to COVID‐19

改变非线性动力系统中的时间估计不确定性及其在 COVID-19 中的应用

• DOI: 10.1002/rnc.5974
• 发表时间: 2022
• 期刊: International Journal of Robust and Nonlinear Control
• 影响因子: 3.9
• 作者: Alisic, Rijad;Paré, Philip E.;Sandberg, Henrik
• 通讯作者: Sandberg, Henrik

The Impact of Vaccine Hesitancy on Epidemic Spreading

疫苗犹豫对流行病传播的影响

• DOI: 10.23919/acc53348.2022.9867327
• 发表时间: 2022
• 期刊: 2022 American Control Conference (ACC
• 作者: Leung, C. H.;Gibbs, María E.;Paré, Philip E.
• 通讯作者: Paré, Philip E.

Estimation and Distributed Eradication of SIR Epidemics on Networks

• DOI: 10.1109/tcns.2023.3306491
• 发表时间: 2024-06-01
• 期刊: IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
• 影响因子: 4.2
• 作者: Zhang，Ciyuan;Leung，Humphrey;Pare，Philip E.
• 通讯作者: Pare，Philip E.

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)
• 作者: Brooks A. Butler;Ciyuan Zhang;I. Walter;N. Nair;Raphael E. Stern;Philip E. Par'e