Real-time models to send medical help and supplies to underserved communities during outbreaks
Developing a dynamic modeling framework for surveillance, prediction, and real-time resource allocation to improve health outcomes during infectious disease outbreaks
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 ID | NIH-11240299 on NIH RePORTER |
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.
Who could benefit from this research
Good fit: 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.
Not a fit: 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.
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 performed: 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, United States
- Clemson University — Clemson, United States (Active)
Researchers
- Principal investigator: Rennert, Lior — Clemson University
- Study coordinator: Rennert, Lior
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 below.