# Automated lab identification and sorting (ALIDAS) for mosquito surveillance

> **NIH ALLCDC R43** · VECTECH, LLC · 2022 · $275,766

## Abstract

Abstract. Mosquitoes are responsible for nearly half-a-million deaths each year. Mosquito
vector control efforts have reduced the burden of mosquito-borne diseases over the past several
decades, but limitations in data-driven vector control decision making hinders progress.
Mosquito surveillance—monitoring an area to understand mosquito species composition,
abundance, and spatial distribution—enables mosquito control organizations to make effective,
efficient, and judicious mosquito control decisions. Despite the importance of mosquito species
identification in surveillance, morphological identification remains highly resource, time, and
labor intensive. Hiring seasonal staff, a significant recurring cost to mosquito control
organizations, is the conventional practice to expand capacity. Entomological expertise can also
vary widely based on individual training and experience, and result in incorrect species
identifications. We seek to develop the first automated lab identification and sorting (ALIDAS)
system for mosquito vector surveillance, to increase surveillance capacity and generate timely
data for targeted mosquito vector control. Computer vision has potential to scale identification
across diverse mosquito species; however, automating the entomological lab workflow to
maximize operational savings, requires a systematic approach to mosquito handling and
movement, while preserving and capturing diagnostic morphological characters in images for
classification. This proposal will utilize novel optics, pneumatics, and computer vision
approaches to isolate, handle, and identify mosquito specimens to species with computer vision.
Ultimately the approaches developed here will allow mosquito control organizations to leverage
image recognition in a practical system that will increase entomological lab capability and
capacity, while reducing operational costs.

## Key facts

- **NIH application ID:** 10484761
- **Project number:** 1R43GH002369-01A1
- **Recipient organization:** VECTECH, LLC
- **Principal Investigator:** Jewell Amanda Brey
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2022
- **Award amount:** $275,766
- **Award type:** 1
- **Project period:** 2022-09-30 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10484761

## Citation

> US National Institutes of Health, RePORTER application 10484761, Automated lab identification and sorting (ALIDAS) for mosquito surveillance (1R43GH002369-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10484761. Licensed CC0.

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