SCC-IRG JST: Hyperlocal Risk Monitoring and Pandemic Preparedness through Privacy-Enhanced Mobility and Social Interactions Analysis

SCC-IRG JST：通过隐私增强的移动性和社交互动分析进行超本地风险监控和流行病防范

• 批准号: 2125530
• 负责人: Li Xiong
• 金额: $ 75万
• 依托单位: Emory University
• 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: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125530&HistoricalAwards=false
• 关键词: SCC; IRG; JST; Hyperlocal; Risk

Hyperlocal risk monitoring is critical for gaining a better estimate of the current and future infection risk at a population level during a pandemic, as well as a better understanding of disparate infection risk in vulnerable groups. However, many challenges remain in enabling hyperlocal risk monitoring and decision making for community-based pandemic preparedness. First, most popular disease prediction models are at a coarse-grained level without considering mobility and social interactions data. Second, a one-size-fits-all approach fails to appropriately address heterogeneity in mobility patterns and interactions, which can be highly community or country specific (e.g., US vs. Japan) and the risks are affected by regional, socioeconomic, behavioral, and cultural differences. Finally, privacy concerns limit the access and use of fine-grained mobility and social interactions data. This project represents a multi-disciplinary collaboration between US and Japanese researchers including lead institutions at Emory University and Kyoto University. The project includes strong community engagement with communities in the US (primarily Georgia and Southern California) and Japan (primarily Kyoto prefecture) as well as local and regional health centers.. The project aims to develop a framework for privacy-enhanced monitoring and analysis of fine-grained mobility and social interactions data to enable hyperlocal risk monitoring and data-driven decision-making. Such hyperlocal situational awareness can help governments and response officials at all levels (from schools and businesses to county and state) for policy making, e.g., open in-person or online; close or partially shut down; and reallocate medical supplies and workforces to vulnerable areas. It can also benefit an individual’s personal decision making in the community, e.g., to avoid high-risk areas. The project includes an integrative research agenda that addresses both technical and social science questions to enable hyperlocal data collection, analysis, and decision making: 1) develop computational and modeling methods for fine-grained risk estimation and scenario analysis (e.g., future estimated risk under partial shutdown) by incorporating real-world mobility and social interactions data; 2) study how mobility patterns, social interactions, behaviors, and risks change and differ by region, socioeconomic status, and country using the US vs. Japan as exemplars; and 3) develop privacy-enhancing technologies and study their social adoption and legal implications for collection and aggregation of mobility and social network data. The team will engage with community stakeholders across the entire data-driven decision-making pipeline including data providers; local public health agencies; local decision makers; and community members. The goal is to not only build a data aggregation and analytics platform but also a feedback loop that enables data-driven policy and decision making while simultaneously enabling social scientists, epidemiologists, and decision makers to steer the data collection, aggregation, and analysis, ultimately enabling better preparation and readiness for future outbreaks. This project is a joint collaboration between the National Science Foundation and the Japan Science and Technology AgencyThis 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.

超本地风险监测对于更好地估计大流行期间人群水平的当前和未来感染风险以及更好地了解弱势群体的不同感染风险至关重要。然而，在实现超地方风险监测和社区大流行防范决策方面仍然存在许多挑战。首先，大多数流行的疾病预测模型都处于粗粒度级别，没有考虑移动性和社会互动数据。其次，“一刀切”的办法无法适当解决流动模式和互动的异质性，这可能是高度社区或国家特定的（例如，风险受地区、社会经济、行为和文化差异的影响。最后，隐私问题限制了细粒度移动和社交互动数据的访问和使用。该项目代表了美国和日本研究人员之间的多学科合作，包括埃默里大学和京都大学的主要机构。 该项目包括与美国（主要是格鲁吉亚和南加州）和日本（主要是京都县）以及当地和区域卫生中心的强有力的社区参与。该项目旨在开发一个框架，用于对细粒度的流动性和社会互动数据进行隐私增强的监测和分析，以实现超本地风险监测和数据驱动的决策。这种超本地态势感知可以帮助各级政府和响应官员（从学校和企业到县和州）制定政策，例如，亲自或在线开放;关闭或部分关闭;并将医疗用品和劳动力重新分配给脆弱地区。它也可以有利于个人在社区中的个人决策，例如，避开高风险区域。该项目包括一个综合研究议程，解决技术和社会科学问题，以实现超本地数据收集，分析和决策：1）开发用于细粒度风险估计和情景分析的计算和建模方法（例如，2）以美国和日本为例，研究流动模式、社会互动、行为和风险如何随地区、社会经济地位和国家而变化和不同;（3）发展隐私--增强技术，研究其社会采用情况以及对收集和汇总移动性和社交网络数据的法律的影响。该团队将与整个数据驱动的决策管道中的社区利益相关者接触，包括数据提供者;当地公共卫生机构;当地决策者;和社区成员。我们的目标不仅是建立一个数据聚合和分析平台，而且是一个反馈回路，使数据驱动的政策和决策，同时使社会科学家，流行病学家和决策者能够引导数据收集，聚合和分析，最终能够更好地为未来的爆发做好准备。该项目是国家科学基金会和日本科学技术委员会的联合合作项目。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。

项目成果

Toward Accurate Spatiotemporal COVID-19 Risk Scores Using High-Resolution Real-World Mobility Data

• DOI: 10.1145/3481044
• 发表时间: 2022-06-01
• 期刊: ACM TRANSACTIONS ON SPATIAL ALGORITHMS AND SYSTEMS
• 影响因子: 1.9
• 作者: Rambhatla, Sirisha;Zeighami, Sepanta;Liu, Yan
• 通讯作者: Liu, Yan

MUter: Machine Unlearning on Adversarial Training Models

MUter：对抗性训练模型的机器遗忘

• 发表时间: 2023
• 期刊: International Conference on Computer Vision
• 作者: Liu, Junxu;Xue Mingsheng;Lou Jian;Zhang, Xiaoyu;Xiong, Li;Qin, Zhan
• 通讯作者: Qin, Zhan

Dynamic Shapley Value Computation

• DOI: 10.1109/icde55515.2023.00055
• 发表时间: 2023-04
• 期刊: 2023 IEEE 39th International Conference on Data Engineering (ICDE)
• 作者: Jiayao Zhang;Haocheng Xia;Qiheng Sun;Jinfei Liu;Li Xiong;Jian Pei;Kui Ren

RobustFed: A Truth Inference Approach for Robust Federated Learning

• DOI: 10.1145/3511808.3557439
• 发表时间: 2021-07
• 期刊: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
• 作者: Farnaz Tahmasebian;Jian Lou;Li Xiong

Differentially-Private Publication of Origin-Destination Matrices with Intermediate Stops

• DOI: 10.48786/edbt.2022.04
• 发表时间: 2022-02
• 作者: Sina Shaham;G. Ghinita;C. Shahabi