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An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Mear-infrared Spectra
PLoS ONE
  • Masabho Peter Milali, Marquette University
  • Samson S. Kiware, Marquette University
  • Nicodem J. Govella, Ifakara Health Institute
  • Fredros Okumu, Ifakara Health Institute
  • Naveen K. Bansal, Marquette University
  • Serdar Bozdag, Marquette University
  • Jacques D. Charlwood, Liverpool School of Tropical Medicine
  • Marta F. Maia, University of Basel, Switzerland
  • Sheila B. Ogoma, Clinton Health Access Initiative
  • Floyd E. Dowell, USDA, Agricultural Research Service, Center for Grain and Animal Health Research
  • George Corliss, Marquette University
  • Maggy T. Sikulu-Lord, The University of Queensland - Brisbane
  • Richard J. Povinelli, Marquette University
Document Type
Article
Language
eng
Publication Date
1-1-2020
Publisher
Public Library of Science (PLoS)
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.

Comments

Published version. PLoS ONE, Vol. 15, No. 6 (2020):1-16 e0234557. DOI. This article is © Public Library of Science (PLoS). Used with permission. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Creative Commons License
Creative Commons Attribution 4.0 International
Citation Information
Masabho Peter Milali, Samson S. Kiware, Nicodem J. Govella, Fredros Okumu, et al.. "An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Mear-infrared Spectra" PLoS ONE (2020) ISSN: 1932-6203
Available at: http://works.bepress.com/george_corliss/4/