Siria, D. J. et al. (2022) Rapid age-grading and species identification of natural mosquitoes for malaria surveillance. Nature Communications, 13, 1501. (doi: 10.1038/s41467-022-28980-8) (PMID:35314683) (PMCID:PMC8938457)
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Abstract
The malaria parasite, which is transmitted by several Anopheles mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of mosquito vectors. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We present a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquito cuticle that simultaneously identifies the species and age class of three main malaria vectors in natural populations. Using spectra from over 40, 000 ecologically and genetically diverse An. gambiae, An. arabiensis, and An. coluzzii females, we develop a deep transfer learning model that learns and predicts the age of new wild populations in Tanzania and Burkina Faso with minimal sampling effort. Additionally, the model is able to detect the impact of simulated control interventions on mosquito populations, measured as a shift in their age structures. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases.
| Item Type: | Articles |
|---|---|
| Additional Information: | This work was funded by the Medical Research Council GCRF Infections Foundation Awards MR/P025501/1 to AD, FB, FOO, HMF, and KW. AD, FB, FO, and SAB were supported by the Royal Society International Collaboration Award ICA/R1/191238 and Bill and Melinda Gates Foundation award OPP1217647. FO was also supported by a Wellcome Trust Intermediate Fellowship in Public Health and Tropical Medicine (Grant Number: WT102350/Z/13), FB by an AXA RF fellowship (14-AXA-PDOC-130) and an EMBO LT fellowship (43-2014). KWand MGJ thank the Engineering and Physical Sciences Research Council (EPSRC) for support through grants EP/K034995/1, EP/N508792/1, and EP/N007417/1, the Leverhulme Trust through Research Project Grant RPG-2018-350, and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 832703). JM is supported by a University of Glasgow Lord Kelvin Adam Smith Studentship. RM-S is grateful for EPSRC support through grants EP/R018634/1 and EP/T00097X/1. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Niang, Dr Abdoulaye and Murray-Smith, Professor Roderick and Mitton, Joshua and Okumu, Professor Fredros and Johnson, Dr Paul and Baldini, Dr Francesco and Wynne, Professor Klaas and Ferguson, Professor Heather and Babayan, Dr Simon and Gonzalez Jimenez, Dr Mario and Mwanga, Emmanuel |
| Authors: | Siria, D. J., Sanou, R., Mitton, J., Mwanga, E. P., Niang, A., Sare, I., Johnson, P. C.D., Foster, G. M., Belem, A. M.G., Wynne, K., Murray-Smith, R., Ferguson, H. M., González-Jiménez, M., Babayan, S. A., Diabaté, A., Okumu, F. O., and Baldini, F. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine College of Medical Veterinary and Life Sciences > School of Life Sciences College of Science and Engineering > School of Chemistry College of Science and Engineering > School of Computing Science |
| Journal Name: | Nature Communications |
| Publisher: | Nature Research |
| ISSN: | 2041-1723 |
| ISSN (Online): | 2041-1723 |
| Copyright Holders: | Copyright © 2022 The Authors |
| First Published: | First published in Nature Communications 13: 1501 |
| Publisher Policy: | Reproduced under a Creative Commons License |
| Related URLs: | |
| Data DOI: | 10.5525/gla.researchdata.1235 |
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