Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis
Abstract Background The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences 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 e...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-05-01
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| Series: | Malaria Journal |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12936-019-2822-y |
| _version_ | 1819110920910012416 |
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| author | Emmanuel P. Mwanga Salum A. Mapua Doreen J. Siria Halfan S. Ngowo Francis Nangacha Joseph Mgando Francesco Baldini Mario González Jiménez Heather M. Ferguson Klaas Wynne Prashanth Selvaraj Simon A. Babayan Fredros O. Okumu |
| author_facet | Emmanuel P. Mwanga Salum A. Mapua Doreen J. Siria Halfan S. Ngowo Francis Nangacha Joseph Mgando Francesco Baldini Mario González Jiménez Heather M. Ferguson Klaas Wynne Prashanth Selvaraj Simon A. Babayan Fredros O. Okumu |
| author_sort | Emmanuel P. Mwanga |
| collection | DOAJ |
| description | Abstract Background The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences 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, mid-infrared (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 reflection-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 classification 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 identified 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 misclassified as goat, and 2% of goat blood meals misclassified 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-effective, 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. |
| first_indexed | 2024-12-22T03:49:23Z |
| format | Article |
| id | doaj.art-14ee3ff3f4a74832afb0b458dbd58b30 |
| institution | Directory Open Access Journal |
| issn | 1475-2875 |
| language | English |
| last_indexed | 2024-12-22T03:49:23Z |
| publishDate | 2019-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Malaria Journal |
| spelling | doaj.art-14ee3ff3f4a74832afb0b458dbd58b302022-12-21T18:40:02ZengBMCMalaria Journal1475-28752019-05-011811910.1186/s12936-019-2822-yUsing mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensisEmmanuel P. Mwanga0Salum A. Mapua1Doreen J. Siria2Halfan S. Ngowo3Francis Nangacha4Joseph Mgando5Francesco Baldini6Mario González Jiménez7Heather M. Ferguson8Klaas Wynne9Prashanth Selvaraj10Simon A. Babayan11Fredros O. Okumu12Environmental Health and Ecological Science Thematic Group, Ifakara Health InstituteEnvironmental Health and Ecological Science Thematic Group, Ifakara Health InstituteEnvironmental Health and Ecological Science Thematic Group, Ifakara Health InstituteEnvironmental Health and Ecological Science Thematic Group, Ifakara Health InstituteEnvironmental Health and Ecological Science Thematic Group, Ifakara Health InstituteEnvironmental Health and Ecological Science Thematic Group, Ifakara Health InstituteInstitute of Biodiversity, Animal Health and Comparative Medicine, University of GlasgowSchool of Chemistry, University of GlasgowInstitute of Biodiversity, Animal Health and Comparative Medicine, University of GlasgowSchool of Chemistry, University of GlasgowInstitute for Disease ModelingInstitute of Biodiversity, Animal Health and Comparative Medicine, University of GlasgowEnvironmental Health and Ecological Science Thematic Group, Ifakara Health InstituteAbstract Background The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences 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, mid-infrared (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 reflection-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 classification 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 identified 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 misclassified as goat, and 2% of goat blood meals misclassified 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-effective, 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.http://link.springer.com/article/10.1186/s12936-019-2822-yMid-infrared spectroscopySupervised machine learningMalariaAnopheles arabiensisMosquito blood mealsIfakara |
| spellingShingle | Emmanuel P. Mwanga Salum A. Mapua Doreen J. Siria Halfan S. Ngowo Francis Nangacha Joseph Mgando Francesco Baldini Mario González Jiménez Heather M. Ferguson Klaas Wynne Prashanth Selvaraj Simon A. Babayan Fredros O. Okumu Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis Malaria Journal Mid-infrared spectroscopy Supervised machine learning Malaria Anopheles arabiensis Mosquito blood meals Ifakara |
| title | Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis |
| title_full | Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis |
| title_fullStr | Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis |
| title_full_unstemmed | Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis |
| title_short | Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis |
| title_sort | using mid infrared spectroscopy and supervised machine learning to identify vertebrate blood meals in the malaria vector anopheles arabiensis |
| topic | Mid-infrared spectroscopy Supervised machine learning Malaria Anopheles arabiensis Mosquito blood meals Ifakara |
| url | http://link.springer.com/article/10.1186/s12936-019-2822-y |
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