Data and code for: Physiological and behavioural resistance of malaria vectors in rural West-Africa: a data mining study to adress their fine-scale spatiotemporal heterogeneity, drivers, and predictability
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24 mai 2023
- doi: 10.23708/LV8GEW
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https://doi.org/10.23708/VJEEMU,
Mots-clés
Medicine, Health and Life Sciences anopheles malaria insecticide resistance behavioural adaptation exophagy early-biting late-biting spatiotemporal distribution statistical modeling data miningSujets proches
Knowledge discovery in data Factual data analysis KDD (Information retrieval) Mining, Data Knowledge discovery in databases Algorithmic knowledge discovery Ague Chills and fever Intermittent fever Malarial feverCiter ce document
Paul Taconet et al., « Data and code for: Physiological and behavioural resistance of malaria vectors in rural West-Africa: a data mining study to adress their fine-scale spatiotemporal heterogeneity, drivers, and predictability », DataSuds, ID : 10.23708/LV8GEW
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These data and scripts are accompanying the manuscript "Physiological and behavioural resistance of malaria vectors in rural West-Africa: a data mining study to adress their fine-scale spatiotemporal heterogeneity, drivers, and predictability" by Paul Taconet, Dieudonne Diloma Soma, Barnabas Zogo, Karine Mouline, Frederic Simard, Alphonsine Amanan Koffi, Roch Kounbobr Dabiré, Cedric Pennetier, and Nicolas Moiroux. The manuscript has been posted as a preprint on biorXiv (https://doi.org/10.1101/2022.08.20.504631). In this data-mining work, we modeled a set of indicators of physiological resistances to insecticide (prevalence of three target-site mutations) and biting behaviours (early- and late-biting, exophagy) of anopheles mosquitoes in two rural areas of West-Africa, located in Burkina Faso and Cote d'Ivoire. To this aim, we used mosquito field collections along with heterogeneous, multisource and multi-scale environmental data. The objectives were i) to assess the small-scale spatial and temporal heterogeneity of the indicators, ii) to better understand their drivers, and iii) to assess their spatio-temporal predictability, at scales that are consistent with operational action. The explanatory variables covered a wide range of potential environmental determinants of vector resistance to insecticide or feeding behaviour: vector control, human availability and nocturnal behaviour, macro and micro-climatic conditions, landscape, etc. ContentsInput datasets and the R script used for the data analyses are provided. Because the models may take very long to fit (due to the size of the raw data), they were pre-fit, saved as .rds files ('R Data Serialization' format), and made available in the "models" folder. The R script used to answer to one of the reviewer's question (reviewer n°1, question n°1) is also included.