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 Agency

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.