Predicting how antimalarial drug resistance spreads with AI
Official title: Predicting the spread of antimalarial drug resistance using deep learning surrogates
Funded at Univ of North Carolina Chapel Hill · NIH-11610185 · Data updated 2026-11-12
A powerful AI model will be built to forecast how resistance to antimalarial drugs might spread in Africa and beyond, helping clinicians and public health teams stay ahead of outbreaks.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Univ of North Carolina Chapel Hill NIH-funded |
| Lab location | 1 site (Chapel Hill, United States) |
| Project start | September 25, 2025 |
| Project end | August 31, 2030 |
| Funding year | 2026 |
| Award (this year) | $327,905 |
| NIH project number | 3R01AI190302-02S1 |
| Project ID | NIH-11610185 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are children affected by malaria in Africa and communities where drug resistance is monitored or emerging.
✕Less likely to help
Populations with no exposure to antimalarial treatment or no access to diagnostic/monitoring data may see limited direct benefit.
What this research studies
This grant supports developing deep learning surrogates that can predict the spread of antimalarial drug resistance by learning from large simulated and real-world data. The project aims to overcome computational barriers in traditional methods, enabling faster and more scalable predictions that inform prevention and response efforts. By focusing on children in Africa, the work seeks to strengthen global health security and reduce malaria-related mortality through better targeting of interventions.
Why it matters
Potential benefit: If successful, the approach could improve early warning and guide timely public health actions to curb resistance and save lives.
How similar studies have gone: Previous deep learning approaches in disease spread modeling show promise, but applying them to antimalarial drug resistance is novel and exploratory.
Where this research is happening
-
Univ of North Carolina Chapel Hill
Chapel Hill, United States Active
Researchers
- Principal investigator: Juliano, Jonathan J — Univ of North Carolina Chapel Hill
- Study coordinator: Juliano, Jonathan J
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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.