# Next generation mosquito control through technology-driven trap development and artificial intelligence guided detection of mosquito breeding habitats

> **NIH NIH R01** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2024 · $47,911

## Abstract

Project Summary
Each year, approximately 400 million people are infected with an arboviral disease from the bite of an Aedes
spp mosquito. Aedes spp. mosquitoes are a leading public health threat due to their high competency to vector
multiple pathogens, their preference to bite humans, and their ability to adapt to new domestic environments. In
the US, reintroduction and establishment of Aedes aegypti and Aedes albopictus mosquito populations has
resulted in local epidemics of Zika, dengue and chikungunya in the past decade. Unfortunately, mosquito control
programs in the US generally operate with limited budgets, forcing the majority of insecticide spraying to be
conducted in reaction to population exposure instead of targeted prevention, which has also contributed to
considerable growth of insecticide resistant populations, yielding a widening gap of infrastructure vulnerability.
Our current proposal aims to leverage existing technologies from non-health disciplines to advance mosquito
detection and abatement. We propose to validate the use of technology-driven mosquito traps that allow for high-
throughput identification and counting of Aedes mosquitos at various life stages to inform decision making when
selecting areas for insecticide spraying and abatement. Additionally, we propose to develop rigorous remote
sensing workflows for identification of neighborhood-level Aedes abundance risk and rapid detection of individual
Aedes mosquito breeding habitats on a household-level. This innovative proposal uses multi-year and real-world
mosquito data from two different metropolitan areas to statistically adjust for variances in geographic ecologies,
urban microclimates, seasonal climate patterns, and annual weather events. Our study will result in low-cost
tools immediately ready for broad distribution and integration by vector control agencies nationally. The
outcomes of our study have promise to directly impact vector control agency’s decision-making processes for
mosquito trapping site selection, inform preventative abetment protocols, and shorten the time required for
mosquito collection and identification. Further, integration of our proposed technology traps and informed site
selection maps will increase overall collection volumes while preserving scarce resources for local vector control
agencies. This proposal has the potential to create a paradigm shift in how we approach vector control globally,
with a targeted intervention resulting in significant economic, environmental, and clinical benefits.

## Key facts

- **NIH application ID:** 11075573
- **Project number:** 3R01AI165560-03S1
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** Sarah Murphy Gunter
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $47,911
- **Award type:** 3
- **Project period:** 2021-09-17 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11075573

## Citation

> US National Institutes of Health, RePORTER application 11075573, Next generation mosquito control through technology-driven trap development and artificial intelligence guided detection of mosquito breeding habitats (3R01AI165560-03S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/11075573. Licensed CC0.

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