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

Crossref DOI link: https://doi.org/10.1186/s13071-021-04851-x

Published Online: 2021-06-29

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Taconet, Paul https://orcid.org/0000-0001-7429-7204
Porciani, Angélique

Soma, Dieudonné Diloma

Mouline, Karine

Simard, Frédéric

Koffi, Alphonsine Amanan

Pennetier, Cedric

Dabiré, Roch Kounbobr

Mangeas, Morgan

Moiroux, Nicolas
Funding

Funding for this research was provided by:

Expertise France (15SANIN213)

Agence Nationale de la Recherche (ANR-16-CE35-008)
License Information

Text and Data Mining valid from 2021-06-29

Version of Record valid from 2021-06-29
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Article History

Received: 14 April 2021

Accepted: 12 June 2021

First Online: 29 June 2021

Declarations

:

: The protocol of this study was reviewed and approved by the Institutional Ethics Committee of the Institut de Recherche en Sciences de la Santé (IEC-IRSS) and registered as N°A06/2016/CEIRES. Mosquito collectors and supervisors gave their written informed consent. They received a vaccine against yellow fever as a prophylactic measure. Collectors were treated free of charge for malaria according to WHO recommendations.

: Not applicable.

: The authors declare that they have no competing interests.

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