Submitted to: Nature Scientific Reports
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/1/2018
Publication Date: N/A
Citation: N/A Interpretive Summary: Mosquitoes transmit malaria, dengue and Zika, which collectively inflict suffering on millions of people each year. The most effective interventions to kill adult mosquitoes are insecticide treated bednets and the indoor spraying of insecticides. The impact of these interventions is under threat due to rapidly growing mosquito resistance to insecticides, and we are in urgent need of methods of evaluating new mosquito control tools in the field. Near infrared spectroscopy (NIRS) shines light through specimens and measures the absorption across a range of wavelengths. As mosquitoes get older, their biochemistry changes, and NIRS, with the use of machine learning techniques, can determine the age of individual mosquito specimens. Here, we compile a database of NIRS experiments carried out on a range of mosquito species and use recently-developed machine learning methods, to considerably boost the predictive performance of NIRS. By prioritising bias minimisation over accuracy alone we can generate precise estimates of the average age of the mosquito population, a very good predictor of the efficacy of interventions which kill adult mosquitoes. A computational malaria transmission model is then used to demonstrate that NIRS can be used to measure the impact of insecticide resistance and the evaluation of control interventions.
Technical Abstract: Mosquito control with bednets, residual sprays or fumigation remains the most effective tool for preventing vector-borne diseases such as malaria, dengue and Zika, though there are no widely used entomological methods for directly assessing its efficacy. Mosquito age is the most informative method for evaluating interventions that kill adult mosquitoes but there is no simple or reliable way of measuring it in the field. Near-Infrared Spectroscopy (NIRS) has been shown to be a promising, high-throughput method that can estimate the age of mosquitoes and differentiate between sibling species. Currently the ability of NIRS to measure mosquito age is biased, and has relatively high individual mosquito measurement error, though its capacity to rigorously monitor mosquito populations in the field has never been assessed. In this study, we use novel machine learning methods to generate more accurate, unbiased estimates of individual mosquito age. These unbiased estimates produce precise population-level measurements, which are relatively insensitive to further increases in NIRS accuracy when feasible numbers of mosquitoes are sampled. Our analyses of Anopheline and Aedine spectra from multiple laboratories using different preservation techniques shows considerable intra-study variability, indicating that that NIRS experimental protocols need to be further refined or else separate calibration datasets may be needed for every population investigated. The utility of NIRS to directly measure the impact of pyrethroid resistance on mosquito control is illustrated, showing how the technology has potential as a highly valuable tool for directly assessing the efficacy of mosquito control interventions.