@article{fdi:010082309,
  title = {{D}ata-driven and interpretable machine-learning modeling to explore the fine-scale environmental determinants of malaria vectors biting rates in rural {B}urkina {F}aso},
  author = {{T}aconet, {P}aul and {P}orciani, {A}ng{\'e}lique and {S}oma, {D}. {D}. and {M}ouline, {K}arine and {S}imard, {F}r{\'e}d{\'e}ric and {K}offi, {A}. {A}. and {P}ennetier, {C}{\'e}dric and {D}abire, {R}. {K}. and {M}angeas, {M}organ and {M}oiroux, {N}icolas},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground {I}mproving the knowledge and understanding of the environmental determinants of malaria vector abundance at fine spatiotemporal scales is essential to design locally tailored vector control intervention. {T}his work is aimed at exploring the environmental tenets of human-biting activity in the main malaria vectors ({A}nopheles gambiae s.s., {A}nopheles coluzzii and {A}nopheles funestus) in the health district of {D}iebougou, rural {B}urkina {F}aso. {M}ethods {A}nopheles 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. {C}orrelation 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. {T}hen, 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. {R}esults {M}eteorological and landscape variables were often significantly correlated with the vectors' biting rates. {M}any nonlinear associations and thresholds were unveiled by the multivariate models, for both meteorological and landscape variables. {F}rom these results, several aspects of the bio-ecology of the main malaria vectors were identified or hypothesized for the {D}iebougou area, including breeding site typologies, development and survival rates in relation to weather, flight ranges from breeding sites and dispersal related to landscape openness. {C}onclusions {U}sing 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. {M}ore broadly, the emerging field of interpretable machine learning has significant potential to help improve our understanding of the complex processes leading to malaria transmission, and to aid in developing operational tools to support the fight against the disease (e.g. vector control intervention plans, seasonal maps of predicted biting rates, early warning systems).},
  keywords = {{M}alaria ; {A}nopheles ; {B}iting behavior ; {A}bundance ; {E}cological niche ; {E}arth observation data ; {S}tatistical modeling ; {C}ross-correlation maps ; {R}andom forest ; {I}nterpretable machine learning ; {A}frica ; {BURKINA} {FASO}},
  booktitle = {},
  journal = {{P}arasites and {V}ectors},
  volume = {14},
  numero = {1},
  pages = {345 [23 ]},
  ISSN = {1756-3305},
  year = {2021},
  DOI = {10.1186/s13071-021-04851-x},
  URL = {https://www.documentation.ird.fr/hor/fdi:010082309},
}