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

Wellcome Open Research

article

Mario González Jiménez¹  Simon A. Babayan²  Pegah Khazaeli¹  Margaret Doyle²  Finlay Walton¹  Elliott Reedy¹  Thomas Glew¹  Mafalda Viana²  Lisa Ranford-Cartwright²  Abdoulaye Niang³  Doreen J. Siria⁴  Fredros O. Okumu²  Abdoulaye Diabaté³  Heather M. Ferguson²  Francesco Baldini²  Klaas Wynne¹ 

[1] School of Chemistry, University of Glasgow;Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow;Department of Medical Biology and Public Health, Institut de Recherche en Science de la Santé;Environmental Health & Ecological Sciences Department, Ifakara Health Institute, Off Mlabani Passage

关键词: Malaria;    Anopheles gambiae;    Anopheles arabiensis;    Vector control;    Machine learning;    Mid-infrared spectroscopy;   

DOI  :  10.12688/wellcomeopenres.15201.3

学科分类：内科医学

来源: Wellcome

【 摘 要 】

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance thatAnopheles 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 speciesAnopheles gambiae andAn. arabiensis, using laboratory colonies. 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 wild mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

【 授权许可】

CC BY