An Autoencoder and Artificial Neural Network-Based Method To Estimate Parity Status Of Wild Mosquitoes From Near-Infrared Spectra

Authors

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 UniversityFollow
Serdar Bozdag, Marquette UniversityFollow
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 UniversityFollow
Maggy T. Sikulu-Lord, The University of Queensland - Brisbane
Richard J. Povinelli, Marquette UniversityFollow

Document Type

Article

Language

eng

Publication Date

6-2020

Publisher

Public Library of Science

Source Publication

PLoS One

Source ISSN

1932-6203

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, June): e0234557. DOI. © 2020 Public Library of Science. Used with permission.

Recommended Citation

Milali, Masabho Peter; Kiware, Samson S.; Govella, Nicodem J.; Okumu, Fredros; Bansal, Naveen K.; Bozdag, Serdar; Charlwood, Jacques D.; Maia, Marta F.; Ogoma, Sheila B.; Dowell, Floyd E.; Corliss, George; Sikulu-Lord, Maggy T.; and Povinelli, Richard J., "An Autoencoder and Artificial Neural Network-Based Method To Estimate Parity Status Of Wild Mosquitoes From Near-Infrared Spectra" (2020). Electrical and Computer Engineering Faculty Research and Publications. 667.
https://epublications.marquette.edu/electric_fac/667