Data-driven and interpretable machine-learning modeling to explore the fine-scale environmental determinants of malaria vectors biting rates in rural Burkina Faso.

Journal: Parasites & vectors
PMID: 34187546

Abstract

BACKGROUND: Improving the knowledge and understanding of the environmental determinants of malaria vector abundance at fine spatiotemporal scales is essential to design locally tailored vector control intervention. This work is aimed at exploring the environmental tenets of human-biting activity in the main malaria vectors (Anopheles gambiae s.s., Anopheles coluzzii and Anopheles funestus) in the health district of Diébougou, rural Burkina Faso.

Authors

  • Paul Taconet
    MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France. paul.taconet@ird.fr.
  • Angélique Porciani
    MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France.
  • Dieudonné Diloma Soma
    MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France.
  • Karine Mouline
    MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France.
  • Frédéric Simard
    MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France.
  • Alphonsine Amanan Koffi
    Institut Pierre Richet (IPR), Bouaké, Côte d'Ivoire.
  • Cedric Pennetier
    MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France.
  • Roch Kounbobr Dabiré
    Institut de Recherche en Sciences de La Santé (IRSS), Bobo-Dioulasso, Burkina Faso.
  • Morgan Mangeas
    ESPACE-DEV, Université Montpellier, IRD, Université Antilles, Université Guyane, Université Réunion, Montpellier, France.
  • Nicolas Moiroux
    MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France.

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