Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

González Jiménez, M. et al. (2019) Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning. Wellcome Open Research, 4, 76. (doi:10.12688/wellcomeopenres.15201.3) (PMID:31544155) (PMCID:PMC6753605)

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Abstract

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis. mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with other mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

Item Type:Articles
Additional Information:Version 3; peer review: 2 approved.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Niang, Dr Abdoulaye and Ranford-Cartwright, Dr Lisa and Okumu, Dr Fredros and Baldini, Dr Francesco and Wynne, Professor Klaas and Ferguson, Professor Heather and Walton, Mr Finlay and Babayan, Dr Simon and Viana, Dr Mafalda and Gonzalez Jimenez, Dr Mario and Doyle, Ms Margaret
Authors: González Jiménez, M., Babayan, S. A., Khazaeli, P., Doyle, M., Walton, F., Reddy, E., Glew, T., Viana, M., Ranford-Cartwright, L., Niang, A., Siria, D. J., Okumu, F. O., Diabaté, A., Ferguson, H. M., Baldini, F., and Wynne, K.
College/School:College of Medical Veterinary and Life Sciences > Institute of Biodiversity Animal Health and Comparative Medicine
College of Science and Engineering > School of Chemistry
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Wellcome Open Research
Publisher:F1000Research
ISSN:2398-502X
Published Online:01 May 2019
Copyright Holders:Copyright © 2019 González Jiménez M et al.
First Published:First published in Wellcome Open Research 4: 76
Publisher Policy:Reproduced under a Creative Commons License

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