Real-time models to send medical help and supplies to underserved communities during outbreaks
Official title: Developing a dynamic modeling framework for surveillance, prediction, and real-time resource allocation to improve health outcomes during infectious disease outbreaks
Funded at Clemson University · NIH-11240299 · Data updated 2026-11-12
This project builds real-time models to help mobile clinics and health teams get testing, treatments, and supplies to people in underserved communities during infectious outbreaks like COVID-19 and HIV.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Clemson University NIH-funded |
| Lab location | 1 site (Clemson, United States) |
| Project start | January 5, 2023 |
| Project end | November 30, 2027 |
| Funding year | 2026 |
| Award (this year) | $589,709 |
| NIH project number | 5R01LM014193-04 |
| Project ID | NIH-11240299 on NIH RePORTER |
Who could benefit from this research
✓Could help
People living in underserved or low-resource communities affected by an infectious disease outbreak, or who use mobile health clinics, would be the main beneficiaries and potential participants.
✕Less likely to help
People living in well-resourced areas with steady access to healthcare or whose conditions are not related to infectious outbreaks may not see direct benefit from this work.
What this research studies
If you live in a low-resource community, this project aims to use health data to spot where outbreaks are growing and which neighborhoods need help most. Researchers will combine statistics, maps, machine learning, and disease-simulation models to predict where cases and needs will rise. Those predictions will be used to guide mobile health clinics and local health agencies so they can send staff, tests, vaccines, or medications to the right places in real time. The toolkit is designed to work with limited local data and to prioritize fair delivery of essential resources during outbreaks.
Why it matters
Potential benefit: If successful, communities could get faster, fairer access to testing, treatment, and vaccines during outbreaks, reducing illness and deaths.
How similar studies have gone: Data-driven modeling helped some regions target COVID-19 responses, but combining geospatial, machine-learning, compartmental, and agent-based models to guide mobile clinics in real time is relatively new.
Where this research is happening
-
Clemson University
Clemson, United States Active
Researchers
- Principal investigator: Rennert, Lior — Clemson University
- Study coordinator: Rennert, Lior
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.