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当前和未来的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，在实现超本地风险监测和评估方面仍然存在许多挑战。首先，最流行的疾病预测模型处于粗粒度水平，没有考虑流动性和社会互动数据；其次，一刀切的方法无法适当解决流动模式的异质性。和互动，这可以是高度特定于社区或国家（例如美国与日本）的风险受到区域、社会经济、行为和文化差异的影响。最后，隐私问题限制了本项目所代表的细粒度移动和社交互动数据的访问和使用。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牵头机构，该项目包括与美国（主要是佐治亚州和南加州）和日本（主要是京都县）以及当地和日本社区的强有力的社区参与。区域卫生该项目旨在开发一个框架，用于对细粒度移动和社交互动数据进行隐私增强监控和分析，以实现超本地风险监控和数据驱动的决策，这种超本地态势感知可以帮助政府和响应官员。各级（从学校和企业到县和州）制定政策，例如亲自开放或在线开放；以及将医疗用品和劳动力重新分配给脆弱地区；这也有利于个人的个人决策。在例如，为了避免高风险区域，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）开发细粒度风险的计算和建模方法。通过结合现实世界的流动性和社会互动数据进行估计和情景分析（例如，部分关闭情况下的未来估计风险）；2）研究流动性模式、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差异；使用美国以日本为例；3）开发隐私增强技术，并研究其社会采用及其对移动和社交网络数据收集和聚合的法律影响。包括数据提供者；当地公共卫生机构和社区成员；目标不仅是建立一个数据聚合和分析平台，而且是一个反馈循环，以支持数据驱动的政策和决策；流行病学家，以及决策者指导数据收集、汇总和分析，最终为未来的疫情爆发做好更好的准备和准备。该项目是美国国家科学基金会和日本科学技术振兴机构之间的联合合作，该奖项反映了国家科学基金会的法定使命，并被视为值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。

项目成果

Personalized Differentially Private Federated Learning without Exposing Privacy Budgets

个性化差异化私有联邦学习而不暴露隐私预算

• 发表时间: 2023-10
• 期刊: 32nd ACM International Conference on Information and Knowledge Management
• 作者: Liu, Junxu;Lou, Jian;Xiong, Li;Meng, Xiaofeng
• 通讯作者: Meng, Xiaofeng

NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks

NeuroSketch：使用神经网络快速近似评估范围聚合查询

• DOI: 10.1145/3588954
• 发表时间: 2023-05
• 期刊: Proceedings of the ACM on Management of Data
• 作者: Zeighami, Sepanta;Shahabi, Cyrus;Sharan, Vatsal
• 通讯作者: Sharan, Vatsal

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

使用高分辨率真实世界移动数据获得准确的时空 COVID-19 风险评分

• DOI: 10.1145/3481044
• 发表时间: 2022-06
• 期刊: ACM Transactions on Spatial Algorithms and Systems
• 影响因子: 1.9
• 作者: Rambhatla, Sirisha;Zeighami, Sepanta;Shahabi, Kameron;Shahabi, Cyrus;Liu, Yan
• 通讯作者: Liu, Yan

A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy

具有用户级差分隐私的时空数据发布的神经方法

• DOI: 10.1145/3588701
• 发表时间: 2023-05
• 期刊: Proceedings of the ACM on Management of Data
• 作者: Ahuja, Ritesh;Zeighami, Sepanta;Ghinita, Gabriel;Shahabi, Cyrus
• 通讯作者: Shahabi, Cyrus

Social Robot Design Challenge: Gathering design requirements from teens

社交机器人设计挑战：收集青少年的设计要求

• DOI: 10.1145/1122445.1122456
• 发表时间: 2019-01
• 期刊: ID
• 作者: Rose, Emma;Björling, Elin A.;Cakmak, Maya
• 通讯作者: Cakmak, Maya