Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus
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 ident...
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | Parasites & Vectors |
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| Online Access: | https://doi.org/10.1186/s13071-024-06209-5 |
| _version_ | 1827310333487742976 |
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| author | Emmanuel P. Mwanga Doreen J. Siria Issa H. Mshani Sophia H. Mwinyi Said Abbasi Mario Gonzalez Jimenez Klaas Wynne Francesco Baldini Simon A. Babayan Fredros O. Okumu |
| author_facet | Emmanuel P. Mwanga Doreen J. Siria Issa H. Mshani Sophia H. Mwinyi Said Abbasi Mario Gonzalez Jimenez Klaas Wynne Francesco Baldini Simon A. Babayan Fredros O. Okumu |
| author_sort | Emmanuel P. Mwanga |
| collection | DOAJ |
| description | 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. Graphical Abstract |
| first_indexed | 2024-04-24T19:59:10Z |
| format | Article |
| id | doaj.art-585997ed105c4a4ab9334b8232baed97 |
| institution | Directory Open Access Journal |
| issn | 1756-3305 |
| language | English |
| last_indexed | 2024-04-24T19:59:10Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Parasites & Vectors |
| spelling | doaj.art-585997ed105c4a4ab9334b8232baed972024-03-24T12:11:53ZengBMCParasites & Vectors1756-33052024-03-011711910.1186/s13071-024-06209-5Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestusEmmanuel P. Mwanga0Doreen J. Siria1Issa H. Mshani2Sophia H. Mwinyi3Said Abbasi4Mario Gonzalez Jimenez5Klaas Wynne6Francesco Baldini7Simon A. Babayan8Fredros O. Okumu9Environmental Health and Ecological Sciences Department, Ifakara Health InstituteEnvironmental Health and Ecological Sciences Department, Ifakara Health InstituteEnvironmental Health and Ecological Sciences Department, Ifakara Health InstituteEnvironmental Health and Ecological Sciences Department, Ifakara Health InstituteEnvironmental Health and Ecological Sciences Department, Ifakara Health InstituteSchool of Biodiversity, One Health and Veterinary Medicine, University of GlasgowSchool of Chemistry, University of GlasgowSchool of Biodiversity, One Health and Veterinary Medicine, University of GlasgowSchool of Biodiversity, One Health and Veterinary Medicine, University of GlasgowEnvironmental Health and Ecological Sciences Department, Ifakara Health InstituteAbstract 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. Graphical Abstracthttps://doi.org/10.1186/s13071-024-06209-5MalariaAnopheles funestusDeep learningMachine learningIfakara Health InstituteMid-infrared spectroscopy |
| spellingShingle | Emmanuel P. Mwanga Doreen J. Siria Issa H. Mshani Sophia H. Mwinyi Said Abbasi Mario Gonzalez Jimenez Klaas Wynne Francesco Baldini Simon A. Babayan Fredros O. Okumu Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus Parasites & Vectors Malaria Anopheles funestus Deep learning Machine learning Ifakara Health Institute Mid-infrared spectroscopy |
| title | Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus |
| title_full | Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus |
| title_fullStr | Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus |
| title_full_unstemmed | Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus |
| title_short | Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus |
| title_sort | rapid classification of epidemiologically relevant age categories of the malaria vector anopheles funestus |
| topic | Malaria Anopheles funestus Deep learning Machine learning Ifakara Health Institute Mid-infrared spectroscopy |
| url | https://doi.org/10.1186/s13071-024-06209-5 |
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