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High throughput estimates of Wolbachia, Zika and chikungunya infection in Aedes aegypti by near-infrared spectroscopy to improve arbovirus surveillance.

Lilha M B SantosMathijs MutsaersGabriela A GarciaMariana R DavidMárcio Galvão PavanMartha T PetersenJessica Corrêa-AntônioDinair Couto-LimaLouis MaesFloyd DowellAnton LordMaggy T Sikulu-LordRafael Maciel-de-Freitas
Published in: Communications biology (2021)
Deployment of Wolbachia to mitigate dengue (DENV), Zika (ZIKV) and chikungunya (CHIKV) transmission is ongoing in 12 countries. One way to assess the efficacy of Wolbachia releases is to determine invasion rates within the wild population of Aedes aegypti following their release. Herein we evaluated the accuracy, sensitivity and specificity of the Near Infrared Spectroscopy (NIRS) in estimating the time post death, ZIKV-, CHIKV-, and Wolbachia-infection in trapped dead female Ae. aegypti mosquitoes over a period of 7 days. Regardless of the infection type, time post-death of mosquitoes was accurately predicted into four categories (fresh, 1 day old, 2-4 days old and 5-7 days old). Overall accuracies of 93.2, 97 and 90.3% were observed when NIRS was used to detect ZIKV, CHIKV and Wolbachia in dead Ae. aegypti female mosquitoes indicating NIRS could be potentially applied as a rapid and cost-effective arbovirus surveillance tool. However, field data is required to demonstrate the full capacity of NIRS for detecting these infections under field conditions.
Keyphrases
  • aedes aegypti
  • zika virus
  • dengue virus
  • high throughput
  • public health
  • big data
  • machine learning
  • deep learning