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Background  The diversity of malaria vector populations, expressing various resistance and/or behavioural patterns could explain the reduced effectiveness of vector control interventions reported in some African countries. A better understanding of the ecology and distribution of malaria vectors is essential to design more effective and sustainable strategies for malaria control and elimination. Here, we analyzed the spatio-temporal risk of the contact between humans and the sympatric  An. funestus  and both M and S molecular forms of  An. gambiae s.s.  in an area of Benin with high coverage of vector control measures with an unprecedented level of resolution.    Methods  Presence-absence data for the three vectors from 1-year human-landing collections in 19 villages were assessed using binomial mixed-effects models according to vector control measures and environmental covariates derived from field and remote sensing data. After 8-fold cross-validations of the models, predictive maps of the risk of the contact between humans and the sympatric  An. funestus  and both molecular M and S forms of  An. gambiae s.s.  were computed.    Results  Model validations showed that the  An. funestus ,  An. gambiae  M form, and S form models provided an excellent (Area Under Curve>0.9), a good (AUC>0.8), and an acceptable (AUC>0.7) level of prediction, respectively. The distribution area of the probability of contact between human and  An. funestus  largely overlaps that of  An. gambiae  M form but this latter showed important seasonal variation.  An. gambiae  S form also showed seasonal variation but with different ecological preferences. Landscape data were useful to discriminate between the species’ distributions.    Conclusions  These results showed that available remote sensing data could help in predicting the human-vector contact for several species of malaria vectors at a village level scale. The predictive maps showed seasonal and spatial variations in the risk of human-vector contact for all three vectors. Such maps could help Malaria Control Programmes to implement more effective vector control strategy by taking into account to the dynamics of malaria vector species.

Modelling the risk of being bitten by malaria vectors in a vector control area in southern Benin, west Africa