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

Author

Listed:
  • Doreen J. Siria

    (Ifakara Health Institute)

  • Roger Sanou

    (Institut de Recherche en Sciences de la Santé (IRSS)/Centre Muraz)

  • Joshua Mitton

    (University of Glasgow
    University of Glasgow
    University of Glasgow)

  • Emmanuel P. Mwanga

    (Ifakara Health Institute
    University of Glasgow)

  • 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
    University of Glasgow)

  • 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
    University of Glasgow)

  • Francesco Baldini

    (University of Glasgow)

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.

Suggested Citation

  • 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-Smit, 2022. "Rapid age-grading and species identification of natural mosquitoes for malaria surveillance," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28980-8
    DOI: 10.1038/s41467-022-28980-8
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    Cited by:

    1. Paola Carrillo-Bustamante & Giulia Costa & Lena Lampe & Elena A. Levashina, 2023. "Evolutionary modelling indicates that mosquito metabolism shapes the life-history strategies of Plasmodium parasites," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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