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

Doreen J. Siria, Roger Sanou, Joshua Mitton, Emmanuel P. Mwanga, Abdoulaye Niang, Issiaka Sare, Paul C. D. Johnson, Geraldine M. Foster, Adrien M. G. Belem, Klaas Wynne, Roderick Murray-Smith, Heather M. Ferguson, Mario González-Jiménez (), Simon A. Babayan (), Abdoulaye Diabaté, Fredros O. Okumu and Francesco Baldini ()
Additional contact information
Doreen J. Siria: Ifakara Health Institute
Roger Sanou: Institut de Recherche en Sciences de la Santé (IRSS)/Centre Muraz
Joshua Mitton: University of Glasgow
Emmanuel P. Mwanga: Ifakara Health Institute
Abdoulaye Niang: Institut de Recherche en Sciences de la Santé (IRSS)/Centre Muraz
Issiaka Sare: Institut de Recherche en Sciences de la Santé (IRSS)/Centre Muraz
Paul C. D. Johnson: University of Glasgow
Geraldine M. Foster: Liverpool School of Tropical Medicine
Adrien M. G. Belem: Université Nazi Boni de Bobo-Dioulasso
Klaas Wynne: University of Glasgow
Roderick Murray-Smith: University of Glasgow
Heather M. Ferguson: Ifakara Health Institute
Mario González-Jiménez: University of Glasgow
Simon A. Babayan: University of Glasgow
Abdoulaye Diabaté: Institut de Recherche en Sciences de la Santé (IRSS)/Centre Muraz
Fredros O. Okumu: Ifakara Health Institute
Francesco Baldini: University of Glasgow

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-28980-8

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