Monitoring the Age of Mosquito Populations Using Near-Infrared Spectroscopy

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Title: Monitoring the Age of Mosquito Populations Using Near-Infrared Spectroscopy
Authors: Lambert, B
Sikulu-Lord, MT
Mayagaya, VS
Devine, G
Dowell, F
Churcher, TS
Item Type: Journal Article
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 metric 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. 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 machine learning methods from the chemometric literature 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. 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.
Issue Date: 27-Mar-2018
Date of Acceptance: 28-Feb-2018
URI: http://hdl.handle.net/10044/1/60154
DOI: https://dx.doi.org/10.1038/s41598-018-22712-z
ISSN: 2045-2322
Publisher: NATURE PUBLISHING GROUP
Journal / Book Title: SCIENTIFIC REPORTS
Volume: 8
Issue: 1
Copyright Statement: © 2018 The Author(s). Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
LEAST-SQUARES REGRESSION
ANOPHELES-GAMBIAE
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
LEAST-SQUARES REGRESSION
ANOPHELES-GAMBIAE
Publication Status: Published
Article Number: ARTN 5274
Online Publication Date: 2018-03-27
Appears in Collections:Faculty of Medicine
Epidemiology, Public Health and Primary Care



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