Discrimination of inherent characteristics of susceptible and resistant strains of Anopheles gambiae by explainable artificial intelligence analysis of flight trajectories.

ABSTRACT: Understanding mosquito behaviours is vital for the development of insecticide-treated nets (ITNs), which have been successfully deployed in sub-Saharan Africa to reduce disease transmission, particularly malaria. However, rising insecticide resistance (IR) among mosquito populations, owing to genetic and behavioural changes, poses a significant challenge. We present a machine learning pipeline that successfully distinguishes between innate IR and insecticide-susceptible (IS) mosquito flight behaviours independent of insecticidal exposure by analysing trajectory data. Data-driven methods are introduced to accommodate common tracking system shortcomings that occur due to mosquito positions being occluded by the bednet or other objects. Trajectories, obtained from room-scale tracking of two I

SUBMITTER: Qureshi YM 

PROVIDER: S-EPMC11862076 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature