This $1,000,000 project grant from the National Science Foundation's Biological Sciences program (CFDA 47.074) supports the development of a Center for Pandemic Decision Science at the University of Texas at Austin from September 1, 2022 to February 29, 2024. The Center aims to advance the integration of complex systems science into pandemic decision-making through a series of multidisciplinary workshops, pilot studies, and educational/community outreach activities. Key goals include identifying...
This Project Grant award from the National Science Foundation's Division of Computing and Communication Foundations supports the development of computational tools and frameworks for bio-social modeling of future pandemics through Arizona State University's PIPP PHASE I: Computational Foundations for Bio-Social Modeling of Unseen Pandemics project. Funded under the Computer and Information Science and Engineering federal grant program (CFDA 47.070), the $807,531 award will advance predictive...
This $1 million Project Grant from the National Science Foundation's Office of Emerging Frontiers and Multidisciplinary Activities supports research at Oregon State University under the NSF Engineering program (CFDA 47.041). The two-year award will fund the establishment of a Pandemic Predictive Intelligence and Policy Center to develop feedback loops between predictive modeling and adaptive response strategies that can attenuate pandemic transmission in urban environments. Key deliverables...
This $200,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) supports the Florida Institute For Human & Machine Cognition Inc. to develop computational theory and models examining the co-evolution of pandemics, information, and human behavior. Key deliverables include interdisciplinary research examining integrated computational models of how information and human psychology impact pandemic transmission. The grantee...
This $968,765 Project Grant awarded by the National Science Foundation's (NSF) Division of Mathematical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) will develop a flexible modeling framework to simulate the impact of various testing-based infection control strategies for respiratory pathogens like SARS-CoV-2. The project aims to improve our ability to control existing respiratory diseases and enhance preparedness for future pandemics. Key deliverables include: Developing a...
The National Science Foundation (NSF) awarded a $200,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of Virginia to develop data-driven, multimodal methods for behavior-based epidemiological modeling. The key objectives are to: Improve techniques for deriving meaningful insights from imperfect, real-world sensor data like mobile phones and search engine logs to capture complex human behaviors in real-time. Couple agent-based disease models...
This $1,999,688 National Science Foundation Project Grant supports the development of predictive intelligence for pandemic prevention through transdisciplinary innovation. Funded by NSF's Computer and Information Science and Engineering program (CFDA #47.070), the two-year award to the University of California, Davis enables advancement of predictive capabilities for forecasting pandemic risk through three key research areas. First, the team will characterize environmental conditions and human...
This $500,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA #47.070) supports research investigating the "pandemic lifecycle" through three collaborating institutions in North Carolina. Duke University serves as the primary awardee and will lead efforts to establish working groups examining fundamental questions around how infectious diseases spread locally and regionally. A pilot study aims to develop simulation...
This National Science Foundation (NSF) award, under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program, provides $700,000 over 3 years to Northeastern University to develop mathematical models and computational algorithms that incorporate group-level behavioral responses into epidemic models. The research aims to better understand how social interactions, risk-mitigating norms, and the formation of pandemic "social bubbles" influence the trajectories of...
Case Western Reserve University was awarded a $2 million Project Grant from the National Science Foundation Division of Computing and Communication Foundations under the Computer and Information Science and Engineering program (CFDA 47.070) to develop a comprehensive, integrated, and intelligent system for early and accurate pandemic prediction, prevention, and preparation. The university will lead a multi-disciplinary research effort involving multiple institutions from July 2022 to December...
This $2 million project grant from the National Science Foundation's Division of Computing and Communication Foundations supports the development of predictive modeling tools to aid pandemic prevention and response efforts. Funded under the Computer and Information Science and Engineering program, the 18-month award to Boston Children's Hospital will plan a future center-scale effort to address gaps in understanding and modeling pandemic potential, impact of interventions, and factors influencing intervention acceptance. Key planned products include determining optimal data sources and methods for assessing pathogen pandemic potential; exploring knowledge gaps in simulating intervention impacts via multi-agent modeling; and establishing best practices for social contagion models to encourage intervention uptake. The grant will also engage students and postdoctoral fellows to communicate science for societal impact and involve the public in a crisis simulation. Success will lead to deploying a novel digital data and multidisciplinary modeling pipeline during a subsequent phase to enhance pandemic surveillance, prediction and mitigation capabilities.