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An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra

Author

Listed:
  • Masabho P Milali
  • Samson S Kiware
  • Nicodem J Govella
  • Fredros Okumu
  • Naveen Bansal
  • Serdar Bozdag
  • Jacques D Charlwood
  • Marta F Maia
  • Sheila B Ogoma
  • Floyd E Dowell
  • George F Corliss
  • Maggy T Sikulu-Lord
  • Richard J Povinelli

Abstract

After mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases in humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which are currently used to determine the parity status of mosquitoes, are very tedious and limited to few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes. In this study, we train an artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets: An. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae s.s collected from Muleba, Tanzania (Muleba-GA); An. gambiae s.s collected from Burkina Faso (Burkina-GA); and An.gambiae s.s from Muleba and Burkina Faso combined (Muleba-Burkina-GA). We train ANN models on datasets with spectra preprocessed according to previous protocols. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train the ANN models. Before the autoencoder was applied, ANN models estimated parity status of mosquitoes in Minepa-ARA, Muleba-GA, Burkina-GA and Muleba-Burkina-GA with out-of-sample accuracies of 81.9±2.8 (N = 274), 68.7±4.8 (N = 43), 80.3±2.0 (N = 48), and 75.7±2.5 (N = 91), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1±2.2% (N = 274), 89.8 ± 1.7% (N = 43), 93.3±1.2% (N = 48), and 92.7±1.8% (N = 91) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively. These results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate the parity status of wild mosquitoes yields models that can be used as an alternative tool to estimate parity status of wild mosquitoes, especially since NIRS is a high-throughput, reagent-free, and simple-to-use technique compared to ovary dissections.

Suggested Citation

  • Masabho P Milali & Samson S Kiware & Nicodem J Govella & Fredros Okumu & Naveen Bansal & Serdar Bozdag & Jacques D Charlwood & Marta F Maia & Sheila B Ogoma & Floyd E Dowell & George F Corliss & Maggy, 2020. "An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    • Handle: RePEc:plo:pone00:0234557
    DOI: 10.1371/journal.pone.0234557
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    References listed on IDEAS

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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Anonymous, 1962. "World Health Organization," International Organization, Cambridge University Press, vol. 16(4), pages 879-880, October.
    3. Masabho P Milali & Maggy T Sikulu-Lord & Samson S Kiware & Floyd E Dowell & Richard J Povinelli & George F Corliss, 2018. "Do NIR spectra collected from laboratory-reared mosquitoes differ from those collected from wild mosquitoes?," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-16, May.
    4. Anonymous, 1962. "World Health Organization," International Organization, Cambridge University Press, vol. 16(1), pages 237-241, January.
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