Rapid age-grading and species identification of natural mosquitoes for malaria surveillance

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:Murray-Smith, Professor Roderick and Baldini, Dr Francesco and Mwanga, Emmanuel and Mitton, Joshua and Okumu, Dr Fredros and Ferguson, Professor Heather and Gonzalez Jimenez, Dr Mario and Niang, Dr Abdoulaye and Johnson, Dr Paul and Wynne, Professor Klaas and Babayan, Dr Simon
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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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
174132Development of a new tool for malaria mosquito surveillance to improve vector controlHeather FergusonMedical Research Council (MRC)MR/P025501/1Institute of Biodiversity, Animal Health and Comparative Medicine
307577AI-MIRS: An Online Platform for Malaria Vector Surveillance in Africa using Artificial Intelligence and Mosquito InfraRed SpectroscopySimon BabayanThe Royal Society (ROYSOC)ICA\R1\191238Institute of Biodiversity, Animal Health and Comparative Medicine
307465AI and InfraRed Spectroscopy to Accelerate Malaria ControlFrancesco BaldiniBill and Melinda Gates Foundation (GATES)OPP 1217647Computing Science
190647Solvation dynamics and structure around proteins and peptides: collective network motions vs. weak interactionsKlaas WynneEngineering and Physical Sciences Research Council (EPSRC)EP/K034995/1Chemistry
171830EPSRC: Institutional Sponsorship 2015 - University of GlasgowMiles PadgettEngineering and Physical Sciences Research Council (EPSRC)EP/N508792/1Computing Science
172399Mapping and controlling nucleationKlaas WynneEngineering and Physical Sciences Research Council (EPSRC)EP/N007417/1Chemistry
303917Delocalised phonon-like modes in organic and bio-moleculesKlaas WynneLeverhulme Trust (LEVERHUL)RPG-2018-350Chemistry
304469CONTROLKlaas WynneEuropean Commission (EC)832703Chemistry
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science
305567QuantIC - The UK Quantum Technoogy Hub in Quantum Enhanced ImagingMiles PadgettEngineering and Physical Sciences Research Council (EPSRC)EP/T00097X/1P&S - Physics & Astronomy