Physiological and behavioural resistance of malaria vectors in rural West-Africa : a data mining study to address their fine-scale spatiotemporal heterogeneity, drivers, and predictability

2024

Article

Taconet Paul, Soma D.D., Zogo B., Mouline Karine, Simard Frédéric, Koffi A.A., Dabiré R.K., Pennetier Cédric, Moiroux Nicolas

Peer Community Journal, 2024, 4, e11 [34 p.] ISSN 2804-3871

Insecticide 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 d'Ivoire. 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.

Lutte [052ANOPAL04]

Fonds IRD [F B010089790]

fdi:010089790