ArticleJournal of medical entomology2023
Accurate age-grading of field-aged mosquitoes reared under ambient conditions using surface-enhanced Raman spectroscopy and artificial neural networks.
Article in Journal of medical entomology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Pixel intensity of wing photos used to infer efficacy of mosquito control interventions against Anopheles gambiae caught during the RIMDAMAL II clinical trial.Scientific reports · 2025Article
- Modelling the effects of adult emergence on the surveillance and age distribution of medically important mosquitoes.PLoS computational biology · 2025Article
- Determining mosquito age using surface-enhanced Raman spectroscopy and artificial neural networks: insights into the influence of origin and sex.Parasites & vectors · 2025Article
- Estimation of population age structure, daily survival rates, and potential to support dengue virus transmission for Florida Keys Aedes aegypti via transcriptional profiling.PLoS neglected tropical diseases · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
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.
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