Understanding the habitats of invasive malaria-carrying mosquitoes in Ethiopia
Official title: Larval Ecology of Invasive Anopheles stephensi in Ethiopia
Funded at University of California-Irvine · NIH-11030801 · Data updated 2026-11-12
This study is looking at how the invasive Anopheles stephensi mosquito, which can spread malaria, lives and breeds in cities, so we can better predict where they might show up and help stop malaria outbreaks.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California-Irvine NIH-funded |
| Lab location | 1 site (Irvine, United States) |
| Project start | February 7, 2024 |
| Project end | December 31, 2025 |
| Funding year | 2025 |
| Award (this year) | $215,127 |
| NIH project number | 5R21AI178613-02 |
| Project ID | NIH-11030801 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for participation or benefit from this research include individuals living in urban regions of Ethiopia who are at risk of malaria due to the presence of Anopheles stephensi.
✕Less likely to help
Patients living in rural areas or regions not affected by Anopheles stephensi may not receive direct benefits from this research.
What this research studies
This research investigates the ecology of Anopheles stephensi, an invasive mosquito species that poses a significant threat to malaria control in Africa. The project aims to identify the environmental and biological factors that influence the habitats of these mosquitoes, particularly in urban areas where they thrive. By utilizing machine learning techniques, the researchers will develop models to predict where these mosquitoes are likely to breed, which is crucial for effective control measures. The findings could help inform targeted interventions to reduce malaria outbreaks linked to this invasive species.
Why it matters
Potential benefit: If successful, this research could lead to improved strategies for controlling malaria transmission in urban areas of Africa.
How similar studies have gone: Previous research has successfully utilized machine learning models to predict mosquito habitats, indicating that this approach has potential for effective application in this context.
Where this research is happening
-
University of California-Irvine
Irvine, United States Active
Researchers
- Principal investigator: Zhou, Guofa — University of California-Irvine
- Study coordinator: Zhou, Guofa
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.