Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. must survive for at least 10 days to possibly transmit malaria. Anopheles 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 species Anopheles and . mid-infrared spectroscopy-based prediction of gambiae An. arabiensis 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 Any reports and responses or comments on the article can be found at the end of the article. approaches, with further improvements to accuracy and expansion for use with other mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.
Background Old mosquitoes are more likely to transmit malaria than young ones. Therefore, accurate prediction of mosquito population age can drastically improve the evaluation of mosquito-targeted interventions. However, standard methods for age-grading mosquitoes are laborious and costly. We have shown that Mid-infrared spectroscopy (MIRS) can be used to detect age-specific patterns in mosquito cuticles and thus can be used to train age-grading machine learning models. However, these models tend to transfer poorly across populations. Here, we investigate whether applying dimensionality reduction and transfer learning to MIRS data can improve the transferability of MIRS-based predictions for mosquito ages. Methods We reared adults of the malaria vector Anopheles arabiensis in two insectaries. The heads and thoraces of female mosquitoes were scanned using an attenuated total reflection-Fourier transform infrared spectrometer, which were grouped into two different age classes. The dimensionality of the spectra data was reduced using unsupervised principal component analysis or t-distributed stochastic neighbour embedding, and then used to train deep learning and standard machine learning classifiers. Transfer learning was also evaluated to improve transferability of the models when predicting mosquito age classes from new populations. Results Model accuracies for predicting the age of mosquitoes from the same population as the training samples reached 99% for deep learning and 92% for standard machine learning. However, these models did not generalise to a different population, achieving only 46% and 48% accuracy for deep learning and standard machine learning, respectively. Dimensionality reduction did not improve model generalizability but reduced computational time. Transfer learning by updating pre-trained models with 2% of mosquitoes from the alternate population improved performance to ~ 98% accuracy for predicting mosquito age classes in the alternative population. Conclusion Combining dimensionality reduction and transfer learning can reduce computational costs and improve the transferability of both deep learning and standard machine learning models for predicting the age of mosquitoes. Future studies should investigate the optimal quantities and diversity of training data necessary for transfer learning and the implications for broader generalisability to unseen datasets.
21The malaria parasite, which is transmitted by several Anopheles mosquito spe-22 42 may now be threatened by insecticide resistance 5 . A further consequence of those 43 mosquito/malaria life cycle dynamics is that accurate and reliable assessment 44 of mosquito age structure is crucial for monitoring the impact of vector control 45 interventions. However, current mosquito age grading methods typically rely on 46 60-year-old techniques based on ovary dissections 6,7 that are slow, labour-intensive 47 and imprecise, and which vary between mosquito species 8 . Many alternatives 48 have been investigated with uneven success 9-14 . Because malaria is transmitted 49 by multiple, often morphologically indistinguishable mosquito species that differ 50 in longevity, behaviours, and vectorial capacity 15,16 , a method that simultaneously 51 estimates vector species and age without relying on time-consuming techniques 52 and expensive reagents would be of great value. 53Like all arthropods, mosquitoes have a cuticle whose chemical composition 54 differs between species and changes with age 8 , which infrared spectroscopy can 55 detect by quantifying how the mosquito cuticle absorbs light 13,17,18 . Early work on 56 infrared spectroscopy for mosquito analysis was restricted to the near-infrared 57
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