D: Computing the Biome
- Funded by National Science Foundation (NSF)
- Total publications:3 publications
Grant number: 2134862
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Key facts
Disease
UnspecifiedStart & end year
20212023Known Financial Commitments (USD)
$2,583,774Funder
National Science Foundation (NSF)Principal Investigator
Janos SztipanovitsResearch Location
United States of AmericaLead Research Institution
Vanderbilt UniversityResearch Priority Alignment
N/A
Research Category
Epidemiological studies
Research Subcategory
Disease surveillance & mapping
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
Individuals, industries, societies, and governments want to stay healthy. They need cost-effective systems to detect biological threats and predict future disease outbreaks as early as possible. COVID-19 acutely and painfully demonstrated the impacts of the unpredicted. The goals of this program, Computing the Biome, are twofold: (1) demonstrate an extensible data and AI platform that continuously monitors and predicts biothreats in a major U.S. city, and (2) create a framework for economic sustainability and global scalability of these results, by empowering businesses and advanced science missions to consume predictions and produce valuable consumer apps and breakthroughs.
This team will produce and interconnect novel data streams ranging from kilometer-scale hyper-local weather, to autonomously identified disease transmitting insects (only millimeters in size), to genomically recognized known and novel viruses (only nanometers in size) - demonstrating that cross-cutting continuous data streams for biothreat detection and prediction can be rapidly unlocked. By combining their expertise in ecology, epidemiology, and virology, the team will design new predictive models and anomaly detectors. This project will develop the first of these high-impact AIs focused on predicting mosquito-borne diseases, which are difficult to control and impact over 600 million people per year. More broadly, the resulting data platform will empower development of new foundational methods for use by the AI community - based on real-world data and grounded in the societal challenges of our age.
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.
This team will produce and interconnect novel data streams ranging from kilometer-scale hyper-local weather, to autonomously identified disease transmitting insects (only millimeters in size), to genomically recognized known and novel viruses (only nanometers in size) - demonstrating that cross-cutting continuous data streams for biothreat detection and prediction can be rapidly unlocked. By combining their expertise in ecology, epidemiology, and virology, the team will design new predictive models and anomaly detectors. This project will develop the first of these high-impact AIs focused on predicting mosquito-borne diseases, which are difficult to control and impact over 600 million people per year. More broadly, the resulting data platform will empower development of new foundational methods for use by the AI community - based on real-world data and grounded in the societal challenges of our age.
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.
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