Biology subjects

Taconet, P.

Publications and source records attributed to Taconet, P..

2 recordsLinked to original sources

bioRxiv2022

Insecticide resistance and biting behaviour of malaria vectors in rural West-Africa : a data mining study to adress their fine-scale spatiotemporal heterogeneity, drivers, and predictability

AO_SCPLOWBSTRACTC_SCPLOWInsecticide resistance and behavioural adaptation of malaria mosquitoes affect the efficacy of long-lasting insecticide nets - currently the main tool for malaria vector control. To develop and deploy complementary, efficient and cost-effective control interventions, a good understanding of the drivers of these physiological and behavioural traits is needed. In this data-mining exercise, we modelled a set of indicators of physiological resistance to insecticide (prevalence of three target-site mutations) and behavioural resistance phenotypes (early- and late-biting, exophagy) of anopheles mosquitoes in two rural areas of West-Africa, located in Burkina Faso and Cote dIvoire. To this aim, we used mosquito field collections along with heterogeneous, multi-source and multi-scale environmental data. The objectives were i) to assess the small-scale spatial and temporal heterogeneity of physiological resistance to insecticide and behavioural resistance phenotypes, 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 behavioural resistance phenotypes : vector control, human availability and nocturnal behaviour, macro and micro-climatic conditions, landscape, etc. The resulting models revealed many statistically significant associations, although their predictive powers were overall weak. We interpreted and discussed these associations in light of several topics of interest, such as : respective contribution of public health and agriculture in the selection of physiological resistances, biological costs associated with physiological resistances, biological mechanisms underlying biting behaviour, and impact of micro-climatic conditions on the time or place of biting. To our knowledge, our work is the first modeling insecticide resistance and feeding behaviour of malaria vectors at such fine spatial scale with such a large dataset of both mosquito and environmental data.

ecology↗

bioRxiv2021

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

BackgroundImproving the knowledge and understanding of the environmental determinants of malaria vectors abundances at fine spatiotemporal scales is essential to design locally tailored vector control intervention. This work 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 Diebougou, rural Burkina Faso. MethodsAnopheles human-biting activity was monitored in 27 villages during 15 months (in 2017-2018), and environmental variables (meteorological and landscape) were extracted from high resolution satellite imagery. A two-step data-driven modeling study was then carried-out. Correlation coefficients between the biting rates of each vector species and the environmental variables taken at various temporal lags and spatial distances from the biting events were first calculated. Then, multivariate machine-learning models were generated and interpreted to i) pinpoint primary and secondary environmental drivers of variation in the biting rates of each species and ii) identify complex associations between the environmental conditions and the biting rates. ResultsMeteorological and landscape variables were often significantly correlated with the vectors biting rates. Many nonlinear associations and thresholds were unveiled by the multivariate models, both for meteorological and landscape variables. From these results, several aspects of the bio-ecology of the main malaria vectors were precised or hypothesized for the Diebougou area, including breeding sites typologies, development and survival rates in relation to weather, flight ranges from breeding sites, dispersal related to landscape openness. ConclusionsUsing high resolution data in an interpretable machine-learning modeling framework proved to be an efficient way to enhance the knowledge of the complex links between the environment and the malaria vectors at a local scale. More broadly, the emerging field of interpretable machine-learning has significant potential to help improving our understanding of the complex processes leading to malaria transmission.

ecology↗