ArticlePLoS computational biology2025
GeneDrive.jl: A decision tool to optimize biological vector control strategies under climate change.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This paper introduces GeneDrive.jl, the first software package to optimize operational planning for the biological control of mosquito disease vectors (access: https://github.com/vnvasquez/GeneDrive.jl). Mosquitoes are responsible for transmitting a significant percentage of the global infectious disease burden, a problem being exacerbated as climate change shifts the range and alters the abundance of these temperature-sensitive arthropods. The efficacy and cost of vector control varies according to species, region, and intervention type. Meanwhile, existing computational tools lack the ability to explicitly tailor interventions for local health objectives and resource limitations. GeneDrive.jl addresses this equity and efficiency gap, which is of particular concern for the tropical regions that both bear the highest mosquito-borne disease burden and are subject to disproportionate climate impacts. The software customizes vector population reduction strategies that employ genetic biocontrol, a broad suite of technologies that alter the genotype or phenotype of mosquito disease vectors, according to specific health goals and financial constraints. It can also be used to characterize risk by analyzing the temperature-responsive dynamics of wildtype vectors. GeneDrive.jl is designed to accommodate two important realities shaping the future of vector-borne disease: first, the genetic-based tools that are defining a new era in control, and second, the uncertainty that increasingly variable and extreme temperatures bring for the climate-sensitive pathogens transmitted by mosquitoes. Written in the Julia programming language, the software provides a 'build once, solve twice' feature wherein users may define a problem, optimize it, and subsequently subject outcomes to scenario-based testing within a single coherent platform. We demonstrate the policy relevance of this scalable open-source framework via case studies featuring the use of Release of Insects with Dominant Lethality (RIDL) to suppress Aedes aegypti populations in the dengue-endemic region of Nha Trang, Vietnam. This work is intended for an interdisciplinary audience and includes a Glossary to facilitate understanding (see S1 Text).
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