Supplementary information 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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This archive is the supplementary material 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.

• Supplementary file 1: Plots and maps of the spatio-temporal distributions of the independent variables used in the models;
• Supplementary file 2: Graphical representation of the modeling workflow;
• Supplementary file 3: Plots of the spatio-temporal distribution of mosquito abundance;
• Supplementary file 4: Results of the GLMM (coefficients / odd ratios, confidence intervals, p-values);
• Supplementary file 5: Evaluation of the explanatory and predictive performance of the models: boxplots of observed resistance status vs. predicted probabilities for all the GLMM and RF models.