Telling how old disease-carrying Aedes aegypti mosquitoes are using light-based fingerprints and AI
A novel approach of age-grading of mosquitoes using SERS and machine learning models
The team is making a low-cost test that uses special light signals and artificial intelligence to find older Aedes aegypti mosquitoes that can spread dengue, Zika, and chikungunya, to help protect communities at risk.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Massachusetts Amherst NIH-funded |
| Lab location | 1 site (Hadley, United States) |
| Project ID | NIH-11226562 on NIH RePORTER |
What this research studies
If you live where dengue, Zika, or chikungunya spread, researchers are developing a field-friendly method to tell how old captured Aedes aegypti mosquitoes are by analyzing tiny molecules released in water using surface-enhanced Raman signals combined with machine learning. They will create reliable lab and field protocols, test how environmental conditions affect results, and refine AI models so the method works outside the lab. The team has already shown promising accuracy in small lab and field tests and now plans broader validation and deployment steps. Successful protocols would let local mosquito-control teams better target the older, infectious mosquitoes that drive outbreaks.
Who could benefit from this research
Good fit: This work is most relevant to communities and public-health programs in regions with Aedes aegypti transmission who can collect mosquitoes for surveillance.
Not a fit: People living in areas without Aedes aegypti mosquitoes or individuals already infected with a mosquito-borne virus would not directly benefit from participating in this research.
Why it matters
Potential benefit: If successful, this could help public-health teams spot and remove the older, infectious mosquitoes sooner, lowering the risk of outbreaks in affected communities.
How similar studies have performed: Early work by the team showed very good accuracy in lab (error <1 day) and promising results in limited field tests (error <2 days), but larger-scale field validation is still needed.
Where this research is happening
Hadley, United States
- University of Massachusetts Amherst — Hadley, United States (Active)
Researchers
- Principal investigator: He, Lili — University of Massachusetts Amherst
- Study coordinator: He, Lili
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.