Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus

Mwanga, E. P., Siria, D. J., Mshani, I. H., Mwinyi, S. H., Abbas, S., Gonzalez Jimenez, M. , Wynne, K. , Baldini, F. , Babayan, S. A. and Okumu, F. O. (2024) Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus. Parasites and Vectors, 17, 143. (doi: 10.1186/s13071-024-06209-5) (PMID:38500231)

Abstract

Background Accurately determining the age and survival probabilities of adult mosquitoes is crucial for understanding parasite transmission, evaluating the effectiveness of control interventions and assessing disease risk in communities. This study was aimed at demonstrating the rapid identification of epidemiologically relevant age categories of Anopheles funestus, a major Afro-tropical malaria vector, through the innovative combination of infrared spectroscopy and machine learning, instead of the cumbersome practice of dissecting mosquito ovaries to estimate age based on parity status. Methods Anopheles funestus larvae were collected in rural south-eastern Tanzania and reared in an insectary. Emerging adult females were sorted by age (1–16 days old) and preserved using silica gel. Polymerase chain reaction (PCR) confirmation was conducted using DNA extracted from mosquito legs to verify the presence of An. funestus and to eliminate undesired mosquitoes. Mid-infrared spectra were obtained by scanning the heads and thoraces of the mosquitoes using an attenuated total reflection–Fourier transform infrared (ATR–FT-IR) spectrometer. The spectra (N = 2084) were divided into two epidemiologically relevant age groups: 1–9 days (young, non-infectious) and 10–16 days (old, potentially infectious). The dimensionality of the spectra was reduced using principal component analysis, and then a set of machine learning and multi-layer perceptron (MLP) models were trained using the spectra to predict the mosquito age categories. Results The best-performing model, XGBoost, achieved overall accuracy of 87%, with classification accuracy of 89% for young and 84% for old An. funestus. When the most important spectral features influencing the model performance were selected to train a new model, the overall accuracy increased slightly to 89%. The MLP model, utilizing the significant spectral features, achieved higher classification accuracy of 95% and 94% for the young and old An. funestus, respectively. After dimensionality reduction, the MLP achieved 93% accuracy for both age categories. Conclusions This study shows how machine learning can quickly classify epidemiologically relevant age groups of An. funestus based on their mid-infrared spectra. Having been previously applied to An. gambiae, An. arabiensis and An. coluzzii, this demonstration on An. funestus underscores the potential of this low-cost, reagent-free technique for widespread use on all the major Afro-tropical malaria vectors. Future research should demonstrate how such machine-derived age classifications in field-collected mosquitoes correlate with malaria in human populations.

• Item Type: Articles
• Additional Information: This study was supported by a Howard Hughes Medical Institute (HHMI)-Gates International Research Scholarship (Grant No. OPP1099295) awarded to FOO and the Medical Research Council (MRC) [MR/P025501/1] awarded to FB. EPM was supported by the Wellcome Trust Masters Fellowship in Tropical Medicine and Hygiene (Grant No. 214643/Z/18/Z). FB is supported by the Academy Medical Sciences Springboard Award (ref: SBF007\100094). SAB is supported by the Bill and Melinda Gates Foundation (INV-030025) and Royal Society (ICA/ R1/191238).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Okumu, Professor Fredros and Baldini, Dr Francesco and Wynne, Professor Klaas and Mshani, Mr Issa and Siria, Doreen Josen and Mwinyi, Sophia Hussein Ally and Babayan, Dr Simon and Gonzalez Jimenez, Dr Mario and Mwanga, Emmanuel
• Authors: Mwanga, E. P., Siria, D. J., Mshani, I. H., Mwinyi, S. H., Abbas, S., Gonzalez Jimenez, M., Wynne, K., Baldini, F., Babayan, S. A., and Okumu, F. O.
• College/School: College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine; College of Science and Engineering > School of Chemistry
• Journal Name: Parasites and Vectors
• Publisher: BioMed Central
• ISSN: 1756-3305
• ISSN (Online): 1756-3305
• Copyright Holders: Copyright: © The Author(s) 2024
• First Published: First published in Parasites and Vectors 17: 143
• Publisher Policy: Reproduced under a Creative Commons licence

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
174132		Development of a new tool for malaria mosquito surveillance to improve vector control	Heather Ferguson	Medical Research Council (MRC)	MR/P025501/1	Institute of Biodiversity, Animal Health and Comparative Medicine