Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning.

ABSTRACT: Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis, using laboratory colonies. Mid-in

SUBMITTER: Gonzalez Jimenez M 

PROVIDER: S-EPMC6753605 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature