Data-driven and interpretable machine-learning modeling to explore the fne-scale environmental determinants of malaria vectors biting rates in rural Burkina Faso

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dc.contributor.authorTaconet, Paul
dc.contributor.authorPorciani, Angélique
dc.contributor.authorSoma, Dieudonné Diloma
dc.date.accessioned2023-06-09T17:40:24Z
dc.date.available2023-06-09T17:40:24Z
dc.date.issued2021-06-29
dc.description.abstractBackground: Improving 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. This work is aimed at exploring the environmental tenets of human-biting activity in the main malaria vectors (Anopheles gambiae s.s., Anopheles coluzzii and Anopheles funestus) in the health district of Diébougou, rural Burkina Faso. Methods: Anopheles 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. Correlation 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. Then, 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. Results: Meteorological and landscape variables were often significantly correlated with the vectors’ biting rates. Many nonlinear associations and thresholds were unveiled by the multivariate models, for both meteorological and landscape variables. From these results, several aspects of the bio-ecology of the main malaria vectors were identified or hypothesized for the Diébougou area, including breeding site typologies, development and survival rates in relation to weather, fight ranges from breeding sites and dispersal related to landscape openness. Conclusions: Using 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. More broadly, the emerging feld of interpretable machine learning has significant potential to help improve our understanding of the complex processes leading to malaria transmission, and to aid in developingen_US
dc.description.sponsorshipACE: Mycotoxin and Food Safetyen_US
dc.identifier.issn1756-3305
dc.identifier.urihttp://hdl.handle.net/123456789/1932
dc.language.isoenen_US
dc.publisherParasites Vectorsen_US
dc.relation.ispartofseriesParasites Vectors;(2021) 14
dc.subjectMalariaen_US
dc.subjectAnophelesen_US
dc.subjectBiting behavioren_US
dc.subjectAbundanceen_US
dc.subjectEcological nicheen_US
dc.subjectEarth observation dataen_US
dc.subjectStatistical modelingen_US
dc.subjectCross-correlation mapsen_US
dc.subjectRandom foresten_US
dc.subjectInterpretable machine learningen_US
dc.subjectAfricaen_US
dc.subjectACE: Mycotoxin and Food Safetyen_US
dc.subjectACEMFSen_US
dc.subjectFederal University of Technology, Minnaen_US
dc.subjectNigeriaen_US
dc.subjectKarine Moulineen_US
dc.subjectFrédéric Simarden_US
dc.titleData-driven and interpretable machine-learning modeling to explore the fne-scale environmental determinants of malaria vectors biting rates in rural Burkina Fasoen_US
dc.typeArticleen_US
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