Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis

Mwanga, E. P. et al. (2019) Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis. Malaria Journal, 18, 187. (doi: 10.1186/s12936-019-2822-y) (PMID:31146762) (PMCID:PMC6543689)

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Background: The propensity of diferent Anopheles mosquitoes to bite humans instead of other vertebrates infuences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Human Blood Index (HBI), currently requires expensive and time-consuming laboratory procedures involving enzyme-linked immunosorbent assays (ELISA) or polymerase chain reactions (PCR). Here, midinfrared (MIR) spectroscopy and supervised machine learning are used to accurately distinguish between vertebrate blood meals in guts of malaria mosquitoes, without any molecular techniques. Methods: Laboratory-reared Anopheles arabiensis females were fed on humans, chickens, goats or bovines, then held for 6 to 8 h, after which they were killed and preserved in silica. The sample size was 2000 mosquitoes (500 per host species). Five individuals of each host species were enrolled to ensure genotype variability, and 100 mosquitoes fed on each. Dried mosquito abdomens were individually scanned using attenuated total refection-Fourier transform infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra (4000 cm−1 to 400 cm−1 ). The spectral data were cleaned to compensate atmospheric water and CO2 interference bands using Bruker-OPUS software, then transferred to Python™ for supervised machine-learning to predict host species. Seven classifcation algorithms were trained using 90% of the spectra through several combinations of 75–25% data splits. The best performing model was used to predict identities of the remaining 10% validation spectra, which had not been used for model training or testing. Results: The logistic regression (LR) model achieved the highest accuracy, correctly predicting true vertebrate blood meal sources with overall accuracy of 98.4%. The model correctly identifed 96% goat blood meals, 97% of bovine blood meals, 100% of chicken blood meals and 100% of human blood meals. Three percent of bovine blood meals were misclassifed as goat, and 2% of goat blood meals misclassifed as human. Conclusion: Mid-infrared spectroscopy coupled with supervised machine learning can accurately identify multiple vertebrate blood meals in malaria vectors, thus potentially enabling rapid assessment of mosquito blood-feeding histories and vectorial capacities. The technique is cost-efective, fast, simple, and requires no reagents other than desiccants. However, scaling it up will require field validation of the findings and boosting relevant technical capacity in affected countries.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Ngowo, Halfan and Gonzalez Jimenez, Dr Mario and Baldini, Dr Francesco and Wynne, Professor Klaas and Babayan, Dr Simon and Okumu, Dr Fredros and Ferguson, Professor Heather
Authors: Mwanga, E. P., Mapua, S. A., Siria, D. J., Ngowo, H. S., Nangacha, F., Mgando, J., Baldini, F., González Jiménez, M., Ferguson, H. M., Wynne, K., Selvaraj, P., Babayan, S. A., and Okumu, F. O.
College/School:College of Science and Engineering > School of Chemistry
College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
Journal Name:Malaria Journal
Publisher:BioMed Central
ISSN (Online):1475-2875
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Malaria Journal 18: 187
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
745811Development of a new tool for malaria mosquito surveillance to improve vector controlHeather FergusonMedical Research Council (MRC)MR/P025501/1RI BIODIVERSITY ANIMAL HEALTH & COMPMED