New computer models to untangle immune responses to respiratory viruses
Official title: Novel Hybrid Computational Models to Disentangle Complex Immune Responses
Funded at University of Idaho · NIH-11179239 · Data updated 2026-11-12
Researchers are combining math-based models and machine learning to better predict how the immune system reacts during viral infections like COVID-19 for people with serious respiratory illness.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Idaho NIH-funded |
| Lab location | 1 site (Moscow, United States) |
| Project start | September 26, 2023 |
| Project end | August 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $199,789 |
| NIH project number | 5R01GM152736-03 |
| Project ID | NIH-11179239 on NIH RePORTER |
Who could benefit from this research
✓Could help
People who have experienced severe respiratory viral infections (for example, serious COVID-19) or who can share clinical data or samples for research would be most relevant to related future studies.
✕Less likely to help
This grant focuses on computer models and animal experiments, so patients should not expect direct or immediate treatment benefits from this work.
What this research studies
This project builds hybrid computer models that merge traditional equation-based descriptions with machine-learning tools to capture complex, time-dependent immune behavior. The team will train these models on existing biological data to reduce the need for thousands of manual parameters. Predictions from the models will then be tested in laboratory mouse coinfection experiments to see which immune changes lead to worse or better outcomes. Results could help researchers design strategies to modulate immune responses in severe respiratory viral infections.
Why it matters
Potential benefit: If successful, the work could improve predictions of disease course and suggest better timing or targets for treatments to reduce deaths from severe respiratory viral infections.
How similar studies have gone: Early work combining mechanistic models and machine learning shows promise, but the hybrid approach remains novel and not yet proven to guide clinical care.
Where this research is happening
-
University of Idaho
Moscow, United States Active
Researchers
- Principal investigator: Hernandez Vargas, Esteban Abelardo — University of Idaho
- Study coordinator: Hernandez Vargas, Esteban Abelardo
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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.