EAGER International Type II: Sustainable International Collaboration in Spatiotemporal Modeling of Human Mobility and Contagion Dynamics for COVID-19

第一部分.为驯服COVID-19疫情，全球经济和社会活动大幅收缩。然而，如果没有适当的预防措施，遏制措施可能很快就会屈服于人员流动。由于在不久的将来没有治疗方法或大规模接种疫苗，因此必须采取有针对性的干预措施，以避免感染死灰复燃，并在恢复正常之前避免日益严重的经济后果。这需要一个准确的时空预测模型，占空间异质性和人体运动。值得注意的是，人类流动和传染动力学的研究不能孤立地进行，越来越多的创新解决方案来自跨境智力互动。然而，随着封锁和旅行限制的发挥，传统的学术活动已经陷入停顿，学术界正在接受虚拟移动和连接。这也引发了对参与者的质量和参与以及长期可持续国际合作的关注。EAGER项目将研究时空人类流动性如何影响COVID-19的传染动力学，这有助于非药物干预的优化设计。它还带来了来自美国的研究学者，日本和澳大利亚将研究COVID-19流行对国际合作的影响，并设计创新机制，使此类跨境合作能够抵御未来的干扰并可持续发展。这种跨学科的合作研究以前所未有的规模整合了流行病动力学，计算社会科学，统计学习和系统工程。主要研究者的团队将致力于以下研究方向：（1）人类流动和传染动力学的时空建模以及与干预政策的联系：将开发一个考虑人类运动和无症状感染的时空分区模型，以描述传染病的动态地理流行和固有的不确定性。(2)协作网络的动态图建模：国际出版物合著将被表示为动态网络，沿着疾病流行，将揭示传染对跨境合作的影响，揭示现有合作伙伴关系中的薄弱协调，并确定国际合作的未来趋势。(3)可持续国际合作的创新机制设计：利用新兴的新型数字基础设施，该团队将共同探索开放科学倡议，用于研究互动，文档，协议制定和评估，以实现弹性和可持续的合作。该项目旨在解读COVID-19对国际合作的影响，并设计战略机制，以促进未来的弹性和可持续的跨境合作。该奖项反映了NSF的法定使命，并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。

Part 1.To tame the COVID‐19 pandemic, staggering pullback in economic and social activities has been imposed globally. Yet, the containment measures can rapidly succumb to human mobility not accompanied by adequate precaution. Absent treatment or vaccine for mass inoculation in the near future, it is indispensable to adopt target-oriented intervention to avert the infection resurgence and eschew mounting economic fallout before the return to normalcy. This entails an accurate spatiotemporal predictive model that accounts for the spatial heterogeneity and human motion. Notably, research in human mobility and contagion dynamics cannot be done in isolation, and innovative solutions are increasingly emanating from cross-border intellectual interactions. However, with lockdown and travel restrictions in play, the traditional academic activities have ground to a screeching halt, and the academia is embracing virtual mobility and connectivity. This has also triggered concerns over the quality and engagement of participants, and sustainable international collaboration over the long run. This EAGER project will investigate how spatiotemporal human mobility will affect the contagion dynamics of COVID-19, which is conducive for optimal design of nonpharmaceutical intervention. It also brings research scholars from the U.S., Japan and Australia to study the impact of COVID-19 prevalence on international collaboration, and design innovative mechanism to render such cross-border cooperation resilient and sustainable against future disturbances.Part 2. This interdisciplinary collaborative research integrates epidemic dynamics, computational social science, statistical learning and systems engineering at an unprecedented scale. The PI’s team will pursue the following research thrusts: (1) Spatiotemporal modeling for human mobility and contagion dynamics and the connection to intervention policy: A spatiotemporal compartmental model considering human motion and asymptomatic infection will be developed to delineate dynamical geographical prevalence of the contagion and the inherent uncertainty. (2) Dynamic graph modeling for collaboration network: International publication co-authorship will be represented as dynamic networks, which along with disease prevalence will uncover the contagion impact on cross-border cooperation, reveal weak coordination in existing partnerships and identify future trend of international collaboration. (3) Innovative mechanism design for sustainable international collaboration: Leveraging the emerging novel digital infrastructure, the team will collectively explore open-science initiatives for research interaction, documentation, protocol formulation and assessment for resilient and sustainable collaboration. This project is anticipated to decipher the impact of COVID-19 on international collaboration and design strategic mechanism to foster future resilient and sustainable cross-border cooperation.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.