Machine learning reveals immediate disruption in mosquito flight when exposed to Olyset nets
Insecticide-treated nets (ITNs) remain a critical intervention in controlling malaria transmission, yet the behavioural adaptations of mosquitoes in response to these interventions are not fully understood. This study examined the flight behaviour of insecticide-resistant (IR) and insecticide-suscep...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Current Research in Parasitology and Vector-Borne Diseases |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667114X25000330 |
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| author | Yasser M. Qureshi Vitaly Voloshin Amy Guy Hilary Ranson Philip J. McCall James A. Covington Catherine E. Towers David P. Towers |
| author_facet | Yasser M. Qureshi Vitaly Voloshin Amy Guy Hilary Ranson Philip J. McCall James A. Covington Catherine E. Towers David P. Towers |
| author_sort | Yasser M. Qureshi |
| collection | DOAJ |
| description | Insecticide-treated nets (ITNs) remain a critical intervention in controlling malaria transmission, yet the behavioural adaptations of mosquitoes in response to these interventions are not fully understood. This study examined the flight behaviour of insecticide-resistant (IR) and insecticide-susceptible (IS) Anopheles gambiae strains around an Olyset net (OL), a permethrin-impregnated ITN, versus an untreated net (UT). Using machine learning (ML) models, we classified mosquito flight trajectories with high balanced accuracy (0.838) and ROC AUC (0.925). Contrary to assumptions that behavioural changes at OL would intensify over time, our findings show an immediate onset of convoluted, erratic flight paths for both IR and IS mosquitoes around the treated net. SHAP analysis identified three key predictive features of OL exposure: frequency of zero-crossings in flight angle change; first quartile of flight angle change; and zero-crossings in horizontal velocity. These suggest disruptive flight patterns, indicating insecticidal irritancy. While IS mosquitoes displayed rapid, disordered trajectories and mostly died within 30 min, IR mosquitoes persisted throughout the 2-h experiments but exhibited similarly disturbed behaviour, suggesting resistance does not fully mitigate disruption. Our findings challenge literature suggesting permethrin’s repellency in solution form, instead supporting an irritant or contact-driven effect when incorporated into net fibres. This study highlights the value of ML-based trajectory analysis for understanding mosquito behaviour, refining ITN configurations and evaluating novel active ingredients aimed at disrupting mosquito flight behaviour. Future work should extend these methods to other ITNs to further illuminate the complex interplay between mosquito behaviour and insecticidal intervention. |
| format | Article |
| id | doaj-art-ee5abcbafa9c43bd8539efefa4d61394 |
| institution | OA Journals |
| issn | 2667-114X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Current Research in Parasitology and Vector-Borne Diseases |
| spelling | doaj-art-ee5abcbafa9c43bd8539efefa4d613942025-08-20T02:07:34ZengElsevierCurrent Research in Parasitology and Vector-Borne Diseases2667-114X2025-01-01710027310.1016/j.crpvbd.2025.100273Machine learning reveals immediate disruption in mosquito flight when exposed to Olyset netsYasser M. Qureshi0Vitaly Voloshin1Amy Guy2Hilary Ranson3Philip J. McCall4James A. Covington5Catherine E. Towers6David P. Towers7School of Engineering, University of Warwick, Coventry, CV4 7AL, UK; Corresponding author.School of Engineering, University of Warwick, Coventry, CV4 7AL, UK; School of Biological and Behavioural Sciences, Queen Mary University of London, E1 4NS, UKVector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UKVector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UKVector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UKSchool of Engineering, University of Warwick, Coventry, CV4 7AL, UKSchool of Engineering, University of Warwick, Coventry, CV4 7AL, UKSchool of Engineering, University of Warwick, Coventry, CV4 7AL, UKInsecticide-treated nets (ITNs) remain a critical intervention in controlling malaria transmission, yet the behavioural adaptations of mosquitoes in response to these interventions are not fully understood. This study examined the flight behaviour of insecticide-resistant (IR) and insecticide-susceptible (IS) Anopheles gambiae strains around an Olyset net (OL), a permethrin-impregnated ITN, versus an untreated net (UT). Using machine learning (ML) models, we classified mosquito flight trajectories with high balanced accuracy (0.838) and ROC AUC (0.925). Contrary to assumptions that behavioural changes at OL would intensify over time, our findings show an immediate onset of convoluted, erratic flight paths for both IR and IS mosquitoes around the treated net. SHAP analysis identified three key predictive features of OL exposure: frequency of zero-crossings in flight angle change; first quartile of flight angle change; and zero-crossings in horizontal velocity. These suggest disruptive flight patterns, indicating insecticidal irritancy. While IS mosquitoes displayed rapid, disordered trajectories and mostly died within 30 min, IR mosquitoes persisted throughout the 2-h experiments but exhibited similarly disturbed behaviour, suggesting resistance does not fully mitigate disruption. Our findings challenge literature suggesting permethrin’s repellency in solution form, instead supporting an irritant or contact-driven effect when incorporated into net fibres. This study highlights the value of ML-based trajectory analysis for understanding mosquito behaviour, refining ITN configurations and evaluating novel active ingredients aimed at disrupting mosquito flight behaviour. Future work should extend these methods to other ITNs to further illuminate the complex interplay between mosquito behaviour and insecticidal intervention.http://www.sciencedirect.com/science/article/pii/S2667114X25000330Insecticide-treated netsInsecticide resistanceMosquito behaviourMachine learningTrajectoriesMalaria control |
| spellingShingle | Yasser M. Qureshi Vitaly Voloshin Amy Guy Hilary Ranson Philip J. McCall James A. Covington Catherine E. Towers David P. Towers Machine learning reveals immediate disruption in mosquito flight when exposed to Olyset nets Current Research in Parasitology and Vector-Borne Diseases Insecticide-treated nets Insecticide resistance Mosquito behaviour Machine learning Trajectories Malaria control |
| title | Machine learning reveals immediate disruption in mosquito flight when exposed to Olyset nets |
| title_full | Machine learning reveals immediate disruption in mosquito flight when exposed to Olyset nets |
| title_fullStr | Machine learning reveals immediate disruption in mosquito flight when exposed to Olyset nets |
| title_full_unstemmed | Machine learning reveals immediate disruption in mosquito flight when exposed to Olyset nets |
| title_short | Machine learning reveals immediate disruption in mosquito flight when exposed to Olyset nets |
| title_sort | machine learning reveals immediate disruption in mosquito flight when exposed to olyset nets |
| topic | Insecticide-treated nets Insecticide resistance Mosquito behaviour Machine learning Trajectories Malaria control |
| url | http://www.sciencedirect.com/science/article/pii/S2667114X25000330 |
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