Siria, D., Sanou, R., Mitton, J., Mwanga, E., Niang, A., Saré, I., Johnson, P. , Wynne, K. , Murray-Smith, R. , Ferguson, H. , Gonzalez Jimenez, M. , Babayan, S. , Diabaté, A., Okumu, F. and Baldini, F. (2022) Rapid age-grading and species identification of natural mosquitoes for malaria surveillance. [Data Collection]
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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 developed 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 developed a deep transfer learning model that learned and predicted the age of new wild populations in Tanzania and Burkina Faso with minimal sampling effort. Additionally, the model was 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.
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| College / School: | College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health and Veterinary Medicine College of Science and Engineering > School of Chemistry College of Science and Engineering > School of Computing Science |
| Date Deposited: | 21 Feb 2022 14:55 |
| URI: | https://researchdata.gla.ac.uk/id/eprint/1235 |
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