@article{fdi:010060779,
  title = {{M}odelling the risk of being bitten by malaria vectors in a vector control area in southern {B}enin, west {A}frica},
  author = {{M}oiroux, {N}icolas and {B}io-{B}angana, {A}. {S}. and {D}jenontin, {A}. and {C}handre, {F}abrice and {C}orbel, {V}incent and {G}uis, {H}.},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground: {T}he diversity of malaria vector populations, expressing various resistance and/or behavioural patterns could explain the reduced effectiveness of vector control interventions reported in some {A}frican 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. {H}ere, we analyzed the spatio-temporal risk of the contact between humans and the sympatric {A}n. funestus and both {M} and {S} molecular forms of {A}n. gambiae s.s. in an area of {B}enin with high coverage of vector control measures with an unprecedented level of resolution. {M}ethods: {P}resence-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. {A}fter 8-fold cross-validations of the models, predictive maps of the risk of the contact between humans and the sympatric {A}n. funestus and both molecular {M} and {S} forms of {A}n. gambiae s.s. were computed. {R}esults: {M}odel validations showed that the {A}n. funestus, {A}n. gambiae {M} form, and {S} form models provided an excellent ({A}rea {U}nder {C}urve>0.9), a good ({AUC}>0.8), and an acceptable ({AUC}>0.7) level of prediction, respectively. {T}he distribution area of the probability of contact between human and {A}n. funestus largely overlaps that of {A}n. gambiae {M} form but this latter showed important seasonal variation. {A}n. gambiae {S} form also showed seasonal variation but with different ecological preferences. {L}andscape data were useful to discriminate between the species' distributions. {C}onclusions: {T}hese 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. {T}he predictive maps showed seasonal and spatial variations in the risk of human-vector contact for all three vectors. {S}uch maps could help {M}alaria {C}ontrol {P}rogrammes to implement more effective vector control strategy by taking into account to the dynamics of malaria vector species.},
  keywords = {{M}alaria ; {A}nopheles ; {R}emote sensing ; {M}odelling ; {H}ost-vector contact ; {BENIN}},
  booktitle = {},
  journal = {{P}arasites and {V}ectors},
  volume = {6},
  numero = {},
  pages = {71},
  ISSN = {1756-3305},
  year = {2013},
  DOI = {10.1186/1756-3305-6-71},
  URL = {https://www.documentation.ird.fr/hor/fdi:010060779},
}