EpiMoRPH: tools to design location-specific plans to control infectious disease
Official title: EpiMoRPH: A simulation environment for generating spatially-refined intervention strategies for the control of infectious disease
Funded at Northern Arizona University · NIH-11374804 · Data updated 2026-11-12
This project creates a computer toolkit to help local public health teams choose location-focused ways to reduce spread of COVID-19 and other infectious diseases.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Northern Arizona University NIH-funded |
| Lab location | 1 site (Flagstaff, United States) |
| Project start | April 1, 2022 |
| Project end | March 31, 2027 |
| Funding year | 2026 |
| Award (this year) | $682,464 |
| NIH project number | 5R01AI168144-05 |
| Project ID | NIH-11374804 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal participants are people and organizations in communities affected by outbreaks, including local public health departments, community leaders, and residents in areas where interventions may be planned.
✕Less likely to help
People seeking direct clinical treatment or those not living in areas engaged with the platform are unlikely to receive direct personal health benefits from this project.
What this research studies
From my point of view, the team is building an online, community-driven platform that automates how detailed spatial disease models are made and compared. The platform will collect benchmark data, let modelers and local health officials run and compare multiple approaches, and crowdsource community input to find what works best in specific places. They will also use mathematical optimization to suggest locally tailored intervention strategies, like where to focus testing, vaccination, or other resources. The goal is to give local teams clear, data-based options for reducing transmission in their neighborhoods.
Why it matters
Potential benefit: If successful, communities could use the toolkit to target interventions more precisely so fewer people get sick and scarce resources are used more effectively.
How similar studies have gone: Epidemic models and decision-support tools were used during COVID-19 with mixed results, and this specific crowdsourced, spatial-optimization approach is newer and less tested at large scale.
Where this research is happening
-
Northern Arizona University
Flagstaff, United States Active
Researchers
- Principal investigator: Mihaljevic, Joseph — Northern Arizona University
- Study coordinator: Mihaljevic, Joseph
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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.