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Article

Limited Spectroscopy Data and Machine Learning for Detection of Zika Virus Infection in Aedes aegypti Mosquitoes

by
Leonardo Reigoto
1,
Rafael Maciel-de-Freitas
2,3,*,
Maggy T. Sikulu-Lord
4,5,
Gabriela A. Garcia
2,
Gabriel Araujo
6 and
Amaro Lima
7
1
Universidade Federal Fluminense, Niteroi 24220-900, RJ, Brazil
2
Laboratório de Mosquitos Transmissores de Hematozoários, Instituto Oswaldo Cruz, Rio de Janeiro 21040-360, RJ, Brazil
3
Department of Entomology and Arbovirology, Bernhard Nocht Institute for Tropical Medicine, 20359 Hamburg, Germany
4
School of the Environment, Faculty of Science, The University of Queensland, Brisbane, QLD 4072, Australia
5
NIRSID Consortium, Brisbane, QLD 4072, Australia
6
Program of Electrical Engineering, Federal Centre of Technological Education of Rio de Janeiro (CEFET/RJ), Campus Maracanã, Rio de Janeiro 20911-020, RJ, Brazil
7
Program of Instrumentation and Applied Optics, Federal Centre of Technological Education of Rio de Janeiro (CEFET/RJ), Campus Maracanã, Rio de Janeiro 20911-020, RJ, Brazil
*
Author to whom correspondence should be addressed.
Trop. Med. Infect. Dis. 2025, 10(11), 308; https://doi.org/10.3390/tropicalmed10110308
Submission received: 10 September 2025 / Revised: 25 October 2025 / Accepted: 27 October 2025 / Published: 29 October 2025
(This article belongs to the Special Issue Beyond Borders—Tackling Neglected Tropical Viral Diseases)
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Abstract

This study presents a technique for categorizing Aedes aegypti mosquitoes infected with the Zika virus under laboratory conditions. Our approach involves the utilization of the near-infrared spectroscopy technique and machine learning algorithms. The model developed utilizes the absorption of light from 350 to 1000 nm. It integrates Linear Discriminant Analysis (LDA) of the signal’s windowed version to exploit non-linearities, along with Support Vector Machine (SVM) for classification purposes. Our proposed methodology can identify the presence of the Zika virus in intact mosquitoes with a balanced accuracy of 96% (row C2HT, average of columns TPR (%) and SPC (%)) when heads/thoraces of mosquitoes are scanned at 4, 7, and 10 days post virus infection. The model was 97.1% (10 DPI, row C2AB, column ACC (%)) accurate for mosquitoes that were used to test it, i.e., mosquitoes scanned 10-days post-infection and mosquitoes whose abdomens were scanned. Notable benefits include its cost-effectiveness and the capability for real-time predictions. This work also demonstrates the role played by different spectral wavelengths in predicting an infection in mosquitoes.
Keywords: Zika virus; machine learning; detection; arboviruses; classification; Support Vector Machine Zika virus; machine learning; detection; arboviruses; classification; Support Vector Machine
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MDPI and ACS Style

Reigoto, L.; Maciel-de-Freitas, R.; Sikulu-Lord, M.T.; Garcia, G.A.; Araujo, G.; Lima, A. Limited Spectroscopy Data and Machine Learning for Detection of Zika Virus Infection in Aedes aegypti Mosquitoes. Trop. Med. Infect. Dis. 2025, 10, 308. https://doi.org/10.3390/tropicalmed10110308

AMA Style

Reigoto L, Maciel-de-Freitas R, Sikulu-Lord MT, Garcia GA, Araujo G, Lima A. Limited Spectroscopy Data and Machine Learning for Detection of Zika Virus Infection in Aedes aegypti Mosquitoes. Tropical Medicine and Infectious Disease. 2025; 10(11):308. https://doi.org/10.3390/tropicalmed10110308

Chicago/Turabian Style

Reigoto, Leonardo, Rafael Maciel-de-Freitas, Maggy T. Sikulu-Lord, Gabriela A. Garcia, Gabriel Araujo, and Amaro Lima. 2025. "Limited Spectroscopy Data and Machine Learning for Detection of Zika Virus Infection in Aedes aegypti Mosquitoes" Tropical Medicine and Infectious Disease 10, no. 11: 308. https://doi.org/10.3390/tropicalmed10110308

APA Style

Reigoto, L., Maciel-de-Freitas, R., Sikulu-Lord, M. T., Garcia, G. A., Araujo, G., & Lima, A. (2025). Limited Spectroscopy Data and Machine Learning for Detection of Zika Virus Infection in Aedes aegypti Mosquitoes. Tropical Medicine and Infectious Disease, 10(11), 308. https://doi.org/10.3390/tropicalmed10110308

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