# Understanding mosquito movement and its relevance to control through genetic analysis

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2021 · $561,892

## Abstract

PROJECT SUMMARY/ABSTRACT:
Dengue, Chikungunya, Zika and other mosquito-borne diseases continue to pose a major global health burden
through much of the world, despite the widespread distribution of insecticide-based tools and antimalarial
drugs. Consequently, there is interest in novel strategies to control these diseases, including the release of
genetically sterile male mosquitoes, mosquitoes transfected with Wolbachia, and mosquitoes engineered with
gene drive systems. The safety and effectiveness of these strategies and considerations regarding trial design
and implementation are critically dependent upon a detailed understanding of mosquito movement at both fine
and broad spatial scales, yet there are major gaps in our understanding of these movement patterns. The
declining cost of genome sequencing and novel methods for analyzing geocoded genomic data provide
opportunities to address these knowledge gaps. In this project, we propose to devise a robust approach for
inferring fine-scale mosquito dispersal patterns and their impact on innovative vector control strategies. We
propose to use in silico simulations of mosquito ecology and preliminary geocoded mosquito genomic data
collected from Fresno, California to determine sampling routines capable of quantifying dispersal patterns,
population sizes and mating patterns using genetic kinship analyses (Aim 1). Results from these analyses will
iteratively inform sampling schemes for two rounds of subsequent collections of Aedes aegypti, the mosquito
vector of dengue, Chikungunya and Zika viruses, in Yishun, Singapore (Aim 2). Genome sequencing and
kinship analyses will be used to quantify Ae. aegypti movement patterns, population sizes and mating
behaviors at this location, and to parameterize spatially-structured 3D models of Ae. aegypti population
dynamics. The resulting models will be used to explore biosafety, trial design and implementation
considerations for novel vector control strategies including: i) population suppression systems such as
Wolbachia-infected males and genetically sterile males, and ii) population replacement systems such as
population transfection with Wolbachia, localized systems such as chromosomal translocations, and non-
localized systems such as homing-based gene drive (Aim 3). We expect the proposed research to lead to the
development of greatly enhanced surveillance strategies to infer fine-scale mosquito movement patterns and
other demographic parameters, and to help inform the safe application of several novel and highly promising
strategies for the control of dengue, Chikungunya and Zika viruses and other devastating mosquito-borne
diseases.

## Key facts

- **NIH application ID:** 10267751
- **Project number:** 5R01AI143698-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** John Macky Marshall
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $561,892
- **Award type:** 5
- **Project period:** 2020-09-21 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10267751

## Citation

> US National Institutes of Health, RePORTER application 10267751, Understanding mosquito movement and its relevance to control through genetic analysis (5R01AI143698-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10267751. Licensed CC0.

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