Abstract

Mosquito-borne diseases are responsible for considerable morbidity and mortality globally. Given the absence of effective vaccines for most arthropod-borne viruses, mosquito control efforts remain the dominant method of disease prevention. Ideal control efforts begin with entomologic surveillance in order to determine the abundance, identity, and infection status of pathogen-vectoring mosquito populations. Traditionally, much of the surveillance work involves morphological species identification by trained entomologists. Limited operational funding and lack of specialized training is a known barrier to surveillance and effective control efforts for many operational mosquito control personnel. Therefore, there is a need for surveillance workflow improvements and rapid mosquito identification methods. Herein, is presented a proof of concept study in which infrared spectroscopy coupled with partial least squares-discriminant analysis was explored as a means of automatically classifying mosquitoes at the species level. The developed method resulted in greater than 94% accuracy for four mosquitoes of public health relevance: Aedes aegypti, Aedes albopictus, Aedes japonicus, and Aedes triseriatus.

© 2020 The Author(s)

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