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

超局部风险监测对于在大流行期间更好地估计人群层面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，在为基于社区的大流行病防范工作进行超地方风险监测和决策方面，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，一刀切的方法不能适当地解决流动性模式和相互作用的异质性，这可能是高度社区或国家特定的（例如，美国与日本），风险受到区域、社会经济、行为和文化差异的影响。最后，隐私问题限制了对细粒度移动和社交交互数据的访问和使用。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（主要是乔治亚州和南加州）和日本（主要是京都县）以及当地和地区卫生中心的社区密切合作。该项目旨在开发一个框架，用于加强对细粒度移动和社交互动数据的隐私监测和分析，以实现超局部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应对官员（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到脆弱地区。它也有利于个人在社区中的个人决策，例如，避免高风险地区。该项目包括一个综合研究议程，解决技术和社会科学问题，以实现超本地数据的收集、分析和决策：1)通过结合现实世界的流动性和社会互动数据，开发用于细粒度风险估计和情景分析（例如，部分关闭下的未来估计风险）的计算和建模方法；2)以美国和日本为例，研究流动模式、社会互动、行为和风险如何因地区、社会经济地位和国家而变化和差异；3)开发增强隐私的技术，并研究其在移动和社交网络数据收集和聚合方面的社会采用和法律影响。该团队将与整个数据驱动决策流程中的社区利益相关者（包括数据提供商）进行接触；地方公共卫生机构；地方决策者；还有社区成员。目标不仅是建立一个数据汇总和分析平台，而且还建立一个反馈回路，使数据驱动的政策和决策能够同时使社会科学家、流行病学家和决策者能够指导数据收集、汇总和分析，最终为未来的疫情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的联合合作项目。该奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响审查标准进行了评估，认为值得支持。

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.