Conference: A Workshop to Train the Next Generation of Scholars at the Interface of AI, Environmental Gradients, and Infectious Disease Modeling
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2521590
Grant search
Key facts
Disease
N/A
Start & end year
20252026Known Financial Commitments (USD)
$64,999Funder
National Science Foundation (NSF)Principal Investigator
Jason RohrResearch Location
United States of AmericaLead Research Institution
University of Notre DameResearch Priority Alignment
N/A
Research Category
13
Research Subcategory
N/A
Special Interest Tags
N/A
Study Type
Not applicable
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Adults (18 and older)
Vulnerable Population
Unspecified
Occupations of Interest
Unspecified
Abstract
The emergence of infectious diseases continues to pose significant global health challenges, exacerbated by environmental changes and increasing human mobility. Vector-borne diseases, such as malaria, dengue, and Lyme disease, are particularly sensitive to climatic and ecological shifts, which influence vector populations and disease transmission patterns. At the same time, advances in artificial intelligence (AI) have revolutionized the ability to analyze complex datasets and predict disease outbreaks with greater accuracy. Despite the growing importance of AI and environmental gradients in infectious disease research, there remains a substantial training gap in integrating these approaches into predictive modeling. This award supports a workshop at the 22nd annual Ecology and Evolution of Infectious Diseases conference to provide graduate students and postdoctoral researchers with hands-on training at the intersection of AI, environmental science, and infectious disease modeling. By equipping early-career scientists with these interdisciplinary tools, this initiative enhances the ability to develop adaptive surveillance systems, inform public health interventions, and mitigate emerging infectious disease threats in a changing world. The workshop will offer an intensive two-and-a-half-day program featuring quantitative and applied training sessions. Participants will engage in parallel tracks, learning to apply Bayesian inference for estimating environmental response functions or utilizing AI techniques to analyze environmentally driven infectious disease patterns. Instructors with expertise in quantitative disease ecology and machine learning will guide trainees through hands-on exercises and collaborative group projects, reinforcing the application of these tools to real-world epidemiological challenges. The workshop's structure ensures that participants develop both technical proficiency and an understanding of how to translate their research into actionable insights. To maximize accessibility, all instructional materials, including recorded sessions and coding resources, will be made publicly available. This initiative not only advances scientific knowledge but also fosters collaboration within the infectious disease research community, helping to prepare the next generation of scientists to tackle global health challenges. 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.