Accurate age-grading of field-aged mosquitoes reared under ambient conditions using surface-enhanced Raman spectroscopy and artificial neural networks

Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools to do so, especially at the critical point where mosquitoes become a risk to humans. In this study, we demonstrated the capability of using surface-enh...

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Bibliographic Details
Published inJournal of medical entomology Vol. 60; no. 5; pp. 917 - 923
Main Authors Gao, Zili, Harrington, Laura C., Zhu, Wei, Barrientos, Luisa M., Alfonso-Parra, Catalina, Avila, Frank W., Clark, John M., He, Lili
Format Journal Article
LanguageEnglish
Published US Entomological Society of America 01.09.2023
Oxford University Press
Subjects
Accuracy
Age
age grading
Aquatic insects
Artificial neural networks
Critical point
Culicidae
Dengue fever
Disease transmission
Entomology
Epidemiology
Females
Machine learning
Malaria
Model accuracy
MODELING/GIS, RISK ASSESSMENT, ECONOMIC IMPACT
Mosquitoes
Neural networks
Pathogens
Public health
Raman spectroscopy
Risk assessment
SERS
Spectroscopy
Temperature effects
Vectors (Biology)
Colombia
mosquitoes
artificial neural networks
age grading
SERS
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Summary:Age-grading mosquitoes are significant because only older mosquitoes are competent to transmit pathogens to humans. However, we lack effective tools to do so, especially at the critical point where mosquitoes become a risk to humans. In this study, we demonstrated the capability of using surface-enhanced Raman spectroscopy and artificial neural networks to accurately age-grade field-aged low-generation (F2) female Aedes aegypti mosquitoes held under ambient conditions (error was 1.9 chronological days, in the range 0–22 days). When degree days were used for model calibration, the accuracy was further improved to 20.8 degree days (approximately equal to 1.4 chronological days), which indicates the impact of temperature fluctuation on prediction accuracy. This performance is a significant advancement over binary classification. The great accuracy of this method outperforms traditional age-grading methods and will facilitate effective epidemiological studies, risk assessment, vector intervention monitoring, and evaluation. Graphical Abstract
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ISSN:0022-2585
1938-2928
DOI:10.1093/jme/tjad067