Floquet Theory for Stochastic Temporal Networks and Optimization Theory for the Design of Schedules for COVID-19

随机时间网络的 Floquet 理论和 COVID-19 时间表设计的优化理论

• 批准号: 2052720
• 负责人: Naoki Masuda
• 金额: $ 24万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2052720&HistoricalAwards=false
• 关键词: Floquet; Theory; Stochastic; Temporal; Networks

No problem facing humanity is more severe and more urgent than COVID-19, and the shelter-in-place approach to social distancing has decimated the U.S. economy. COVID-19 social distancing may be required for a long period for a multitude of reasons including the uncertainty of the effectiveness of vaccines on emerging strains. Therefore, it is desired to identify mathematically principled solutions that strike an optimal balance between productivity and risk to infection through developing a “science and engineering for social distancing”. Thus motivated, the PIs will develop (1) novel mathematical theory to better and more rigorously understand epidemic spreading on social-contact networks that undergo weekly and/or daily cycles; and (2) optimization theory to design realistic social-contact networks (e.g., using curfews for community and course scheduling for academic institutions) that both mitigate pandemics and allow an acceptable level of social/economic activity. These techniques will be applied to social-network datasets related to COVID-19 to validate theory and obtain practical knowledge. These mathematical and computational tools and related datasets will support policy makers for educational institutions, large companies, and governing bodies as they manage the economic and health impacts of COVID-19 as well as future pandemics. The first goal of the project is to extend Floquet theory of ordinary differential equations to (a) characterize epidemic spreading on social-contact networks that are stochastic, time-varying, and periodic; (b) identify and analyze novel behaviors for disease dynamics that arise because the social-network’s periodicity and the disease progression change at similar time scales; and (c) study the effects of network structures that contribute to disease localization (e.g., in hub nodes and communities in the network). These questions remain underexplored for dynamically changing networks. The second goal of the project is to develop network optimization theory to design mathematically principled social-distancing protocols that can both suppress COVID-19 spreading and also maintain an acceptable level of economic and social activity. Specifically, network perturbation and optimization techniques for Floquet decompositions will be developed, and then they will be combined with non-convex optimization algorithms. To date, mathematical or even computational foundations for social-contact engineering in dynamically changing contact networks due to human activity are lacking. Finally, these techniques will be applied to social-network datasets related to COVID-19 which provide summarized and anonymized movements of individuals.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.

人类面临的问题没有比新冠病毒 (COVID-19) 更严重、更紧迫，而就地避难所采取的保持社交距离的做法已经摧毁了美国经济，出于多种原因，可能需要在很长一段时间内保持社交距离。因此，希望通过开发“社会疏远的科学和工程”来确定数学原理的解决方案，以在生产力和感染风险之间实现最佳平衡。开发 (1) 新颖数学理论，以更好、更严格地理解流行病在每周和/或每日循环的社交接触网络上的传播；(2) 优化理论，以设计现实的社交接触网络（例如，对学术机构使用宵禁和课程安排）； ）既可以减轻流行病，又可以使社会/经济活动达到可接受的水平。这些技术将应用于与 COVID-19 相关的社交网络数据集，以验证所获得的理论和实践知识。这些数学和计算工具以及相关数据集将支持政策。制造商该项目的首要目标是将常微分方程的 Floquet 理论扩展到 (a) 描述流行病传播的特征。随机、时变和周期性的社交接触网络；(b) 识别动态并分析由于社交网络的周期性和疾病进展在相似的时间尺度上变化而出现的新行为；效果该项目的第二个目标是开发网络优化理论来设计数学原理的社交距离协议。具体来说，将开发用于 Floquet 分解的网络扰动和优化技术，然后将它们与非凸优化相结合。迄今为止，由于人类活动而动态变化的联系网络还缺乏数学甚至计算基础。最后，这些技术将应用于与 COVID-19 相关的社交网络数据集，从而提供汇总和匿名的运动。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

Simplicial cascades are orchestrated by the multidimensional geometry of neuronal complexes

• DOI: 10.1038/s42005-022-01062-3
• 发表时间: 2022-01
• 期刊: Communications Physics
• 影响因子: 5.5
• 作者: Bengi Kilic;D. Taylor

Coupling Asymmetry Optimizes Collective Dynamics Over Multiplex Networks

• DOI: 10.1109/tnse.2023.3325278
• 发表时间: 2021-06
• 期刊: IEEE Transactions on Network Science and Engineering
• 影响因子: 6.6
• 作者: Z. Song;D. Taylor

Dimension reduction of dynamical systems on networks with leading and non-leading eigenvectors of adjacency matrices

具有邻接矩阵的前导和非前导特征向量的网络动力系统的降维

• DOI: 10.1103/physrevresearch.4.023257
• 发表时间: 2022
• 期刊: Physical Review Research
• 影响因子: 4.2
• 作者: Masuda, Naoki;Kundu, Prosenjit
• 通讯作者: Kundu, Prosenjit

Grass-roots optimization of coupled oscillator networks

耦合振荡器网络的基层优化

• DOI: 10.1103/physreve.106.034202
• 发表时间: 2022
• 期刊: Physical Review E
• 影响因子: 2.4
• 作者: Chamlagai, Pranick R.;Taylor, Dane;Skardal, Per Sebastian
• 通讯作者: Skardal, Per Sebastian

Social network analysis of manga: similarities to real-world social networks and trends over decades

漫画的社交网络分析：与现实世界社交网络的相似之处以及几十年来的趋势

• DOI: 10.1007/s41109-023-00604-0
• 发表时间: 2023
• 期刊: Applied Network Science
• 影响因子: 2.2
• 作者: Sugishita, Kashin;Masuda, Naoki
• 通讯作者: Masuda, Naoki