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Finding a Husband: Using Explainable AI to Define Male Mosquito Flight Differences

Qureshi, Yasser M; Voloshin, Vitaly; Facchinelli, Luca; McCall, Philip J; Chervova, Olga; Towers, Cathy E; Covington, James A; (2023) Finding a Husband: Using Explainable AI to Define Male Mosquito Flight Differences. Biology , 12 (4) , Article 496. 10.3390/biology12040496. Green open access

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Abstract

Mosquito-borne diseases account for around one million deaths annually. There is a constant need for novel intervention mechanisms to mitigate transmission, especially as current insecticidal methods become less effective with the rise of insecticide resistance among mosquito populations. Previously, we used a near infra-red tracking system to describe the behaviour of mosquitoes at a human-occupied bed net, work that eventually led to an entirely novel bed net design. Advancing that approach, here we report on the use of trajectory analysis of a mosquito flight, using machine learning methods. This largely unexplored application has significant potential for providing useful insights into the behaviour of mosquitoes and other insects. In this work, a novel methodology applies anomaly detection to distinguish male mosquito tracks from females and couples. The proposed pipeline uses new feature engineering techniques and splits each track into segments such that detailed flight behaviour differences influence the classifier rather than the experimental constraints such as the field of view of the tracking system. Each segment is individually classified and the outcomes are combined to classify whole tracks. By interpreting the model using SHAP values, the features of flight that contribute to the differences between sexes are found and are explained by expert opinion. This methodology was tested using 3D tracks generated from mosquito mating swarms in the field and obtained a balanced accuracy of 64.5% and an ROC AUC score of 68.4%. Such a system can be used in a wide variety of trajectory domains to detect and analyse the behaviours of different classes, e.g., sex, strain, and species. The results of this study can support genetic mosquito control interventions for which mating represents a key event for their success.

Type: Article
Title: Finding a Husband: Using Explainable AI to Define Male Mosquito Flight Differences
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/biology12040496
Publisher version: https://doi.org/10.3390/biology12040496
Language: English
Additional information: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Keywords: mosquitoes; machine learning; trajectory analysis; explainable artificial intelligence; mosquito behaviour; classification; insect tracking
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Cancer Bio
URI: https://discovery.ucl.ac.uk/id/eprint/10167993
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