Spatio-temporal analysis of abundances of three malaria vector species in southern Benin using zero-truncated models.

BACKGROUND: A better understanding of the ecology and spatial-temporal distribution of malaria vectors is essential to design more effective and sustainable strategies for malaria control and elimination. In a previous study, we analyzed presence-absence data of An. funestus, An. coluzzii, and An. gambiae s.s. in an area of southern Benin with high coverage of vector control measures. Here, we further extend the work by analysing the positive values of the dataset to assess the determinants of the abundance of these three vectors and to produce predictive maps of vector abundance. METHODS: Positive counts of the three vectors were assessed using negative-binomial zero-truncated (NBZT) mixed-effect models according to vector control measures and environmental covariates derived from field and remote sensing data. After 8-fold cross-validation of the models, predictive maps of abundance of the sympatric An. funestus, An. coluzzii, and An. gambiae s.s. were produced. RESULTS: Cross-validation of the NBZT models showed a satisfactory predictive accuracy. Almost all changes in abundance between two surveys in the same village were well predicted by the models but abundances for An. gambiae s.s. were slightly underestimated. During the dry season, predictive maps showed that abundance greater than 1 bite per person per night were observed only for An. funestus and An. coluzzii. During the rainy season, we observed both increase and decrease in abundance of An. funestus, which are dependent on the ecological setting. Abundances of both An. coluzzii and An. gambiae s.s. increased during the rainy season but not in the same areas. CONCLUSIONS: Our models helped characterize the ecological preferences of three major African malaria vectors. This works highlighted the importance to study independently the binomial and the zero-truncated count processes when evaluating vector control strategies. The study of the bio-ecology of malaria vector species in time and space is critical for the implementation of timely and efficient vector control strategies.

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

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.