Explainable artificial intelligence for analysing mosquito flight behaviour

Mosquitoes are among the deadliest organisms on Earth, transmitting diseases that threaten global health. Malaria alone kills over half a million people annually, mostly young children. While insecticide-treated bed nets and indoor residual spraying have reduced transmission, these measures are increasingly compromised by insecticide-resistant mosquito populations and their behavioural adaptations. Despite well-documented genetic resistance, behavioural resistance remains poorly understood but critical to intervention efficacy. This thesis helps to address this gap by developing a machine learning and explainable AI (XAI) framework to analyse mosquito flight behaviours, focusing on identifying traits linked to resistance. The framework integrates preprocessing, trajectory segmentation, feature extraction, classification, and SHapley Additive exPlanations (SHAP) to provide interpretable insights. New techniques addressed tracking challenges like occlusions and spatial distortions, ensuring robustness. Three experimental conditions were analysed: (1) mating swarm data to classify males from non-males, (2) behavioural differences between insecticide-susceptible (IS) and insecticide-resistant (IR) mosquitoes near untreated nets, and (3) analogous trials involving permethrin-treated nets. The primary contributions of this thesis include the identification of behavioural signatures associated with insecticide resistance. IR mosquitoes exhibited reduced flight speeds, more direct paths, and lower fractal dimensions than IS mosquitoes, suggesting goal-directed flight. Exposure to permethrin-treated nets triggered erratic flight patterns in both IR and IS mosquitoes, with SHAP analysis identifying key features predictive of irritancy. Comparative analyses using this framework evaluated the performance of different tracking systems, confirming that 2D telecentric imaging matches 3D stereoscopic tracking performance, with singlecamera setups offering similar approximations at long ranges. Additionally, synthetic trajectories enhanced model interpretability by probing decision boundaries. The framework’s versatility and interpretability provide insights that could inform the design of interventions targeting mosquito vulnerabilities, with potential applications to other complex behavioural systems where understanding subtle patterns is essential.