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

Qureshi, Yasser M., Voloshin, Vitaly, Facchinelli, Luca ORCID: https://orcid.org/0000-0002-8987-1472, McCall, Philip ORCID: https://orcid.org/0000-0002-0007-3985, Chervova, Olga, Towers, Cathy E., Covington, James A. and Towers, David P. (2023) 'Finding a Husband: Using Explainable AI to Define Male Mosquito Flight Differences'. Biology, Vol 12, Issue 4, e496.

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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.

Item Type:	Article
Subjects:	WC Communicable Diseases > WC 20 Research (General) WC Communicable Diseases > Tropical and Parasitic Diseases > WC 680 Tropical diseases (General) WC Communicable Diseases > Tropical and Parasitic Diseases > WC 695 Parasitic diseases (General) WC Communicable Diseases > Tropical and Parasitic Diseases > WC 750 Malaria
Faculty: Department:	Biological Sciences > Vector Biology Department
Digital Object Identifer (DOI):	https://doi.org/10.3390/biology12040496
SWORD Depositor:	JISC Pubrouter
Depositing User:	JISC Pubrouter
Date Deposited:	03 Apr 2023 10:49
Last Modified:	03 Apr 2023 14:17
URI:	https://archive.lstmed.ac.uk/id/eprint/22222

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