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

Abstract

These authors contributed equally to this work.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 12 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 total population sizes. However, estimating mosquito age is currently a slow, imprecise, and labour-intensive process that can only distinguish under-from over-fourday-old female mosquitoes. Here, we demonstrate a machine-learning based approach that utilizes midinfrared spectra of mosquitoes to characterize simultaneously, and with unprecedented accuracy, both age and species identity of females of the malaria vectors Anopheles gambiae and An. arabiensis mosquitoes within their respective populations. The prediction of the age structures was statistically indistinguishable from true modelled distributions. The method has a negligible cost per mosquito, does not require highly trained personnel, is substantially faster than current techniques, and so can be easily applied in both laboratory and field settings. Our results show that, with larger mid-infrared spectroscopy data sets, this technique can be further improved and expanded to vectors of other diseases such as Zika and Dengue.

Ability of near-infrared spectroscopy and chemometrics to predict the age of mosquitoes reared under different conditions

“…Spectral analyses suggest that water absorbance peaks may contribute to the variation observed here, and reflect the physiological state of the mosquito or the immediate environment. Water creates strong NIRS signals that may dominate other signatures in the cuticle [16] and may be masking important, age-related spectra; however, this cannot be confirmed unless there are additional studies performed on dried mosquitoes. All adult mosquitoes used in this study were cage-reared with ad libitum access to 10% sugar solution; therefore, it is unknown if water signals directly influences spectral data collected for age grading, and whether moisture content in a mosquito is a limitation for NIR mosquito studies.…”

“…The application of NIRS and chemometrics to the age classification of insects would benefit from a better understanding of the factors that affect absorbance and the challenges they pose to accurate prediction. A spectral database defined in terms of its causative physiological or biochemical drivers might allow for data analyses to be performed using only the most relevant regions, as can be seen in a mosquito mid-infrared study [16]. This method has also been used in various other NIRS studies, where there is emphasis on an individual wavelength related to the detection of a specific chemical bond [40,41].…”

“…This has historically been performed using Partial Least Squares (PLS) regression and the software GRAMS IQ (Thermo Fisher Scientific, MA, USA). It has been postulated that prediction accuracy might be improved using more complex analytical and model-building techniques [15,16] and that pre-processing data cleaning might also improve performance [3].…”

Ability of near-infrared spectroscopy and chemometrics to predict the age of mosquitoes reared under different conditions

“…Water creates strong NIRS signals that may dominate other signatures in the cuticle [16] and may be masking important, age-related spectra; however this cannot be confirmed unless there are additional studies performed on dried mosquitoes. All adult mosquitoes used in this study were cage-reared with ad libitum access to 10% sugar solution; therefore, it is unknown if water signals directly influences spectral data collected for age grading, and whether moisture content in a mosquito is a limitation for NIR mosquito studies.…”

Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis

“…Examples include detection of endosymbionts such as Wolbachia bacteria, and pathogens such as Plasmodium and Zika virus in mosquitoes [28][29][30][31]. Such approaches have also been used for estimating age of disease-transmitting mosquitoes [32][33][34][35][36][37][38], distinguishing between vector species [32,38,39] and assessing their blood feeding histories [40], all of which directly influence malaria transmission.…”