CAREER: Building Next-Generation Epidemic Intelligence: Forecasting, Intervention, and Surveillance

Infectious disease outbreaks pose serious threats to global health, economic stability, and societal well-being. An effective response system needs to answer the following key questions in a timely manner: how will a disease spread, what interventions can control them, and how to monitor populations to enable early warnings? However, current approaches often rely on fragmented or delayed data, such as clinical case reports, incomplete testing coverage, and/or the inability to see how infected people move and interact. These factors make it difficult to combine different sources of information and adapt to rapidly changing conditions. These limitations can delay response efforts and reduce their effectiveness. This project aims to develop next-generation epidemic intelligence systems that improve how public health agencies forecast, manage, and monitor infectious diseases. By enabling earlier detection, more targeted interventions, and better situational awareness, the project will strengthen public health infrastructure, support informed decision-making, and enhance resilience to future outbreaks. The project also contributes to education by training students at multiple levels, engaging K-12 learners, and providing open resources to increase participation in data science and public health. To meet these goals, this project develops a unified, data-driven framework that integrates epidemic forecasting, intervention planning, and surveillance under diverse and evolving data conditions. Specifically, the project focuses on three objectives: (1) advancing epidemic forecasting by combining established transmission models with modern machine learning to enable multi-scale prediction across fine-grained contact networks and large-scale population dynamics; (2) designing context-aware intervention strategies that adapt to local conditions, such as population density, regional transmission risk, and operational constraints, while balancing effectiveness and cost; and, (3) developing resource-efficient surveillance methods that optimize diagnostic testing and data collection across multiple sources, including clinical reports and wastewater signals. To address changing epidemic conditions, the framework incorporates adaptive learning mechanisms that detect shifts in data patterns and update models and policies accordingly. The project will be evaluated using real-world datasets and simulation environments, and the resulting methods will provide scalable and robust tools for epidemic analysis, contributing broadly to the fields of data science, network modeling, and public health. 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. NSF Award ID: 2543168 | Program: 01003031DB NSF RESEARCH & RELATED ACTIVIT,01002930DB NSF RESEARCH & RELATED ACTIVIT,01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Chen Chen | Institution: University of Virginia Main Campus, CHARLOTTESVILLE, VA | Award Amount: $420,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2543168 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2543168.html