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

• Published in: Parasites & vectors Vol. 14; no. 1; pp. 1 - 345
• Main Authors: Taconet, Paul, Porciani, Angélique, Soma, Dieudonné Diloma, Mouline, Karine, Simard, Frédéric, Koffi, Alphonsine Amanan, Pennetier, Cedric, Dabiré, Roch Kounbobr, Mangeas, Morgan, Moiroux, Nicolas
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 29.06.2021; BioMed Central; BMC
• Subjects: Abundance; Animal biology; Anopheles; Artificial Intelligence; Atmospheric models; Biting; Biting behavior; Breeding sites; Coefficients; Computer Science; Correlation coefficient; Correlation coefficients; Datasets; Determinants; Disease control; Disease transmission; Dispersal; Distribution; Early warning systems; Earth observation data; Ecological effects; Ecological niche; Ecology; Ecology, environment; Environmental conditions; Health; Health aspects; High resolution; Households; Human diseases; Hypotheses; Image resolution; Imagery; Invertebrate Zoology; Learning algorithms; Life Sciences; Machine learning; Malaria; Methods; Modelling; Mosquitoes; Multivariate analysis; Prevalence studies (Epidemiology); Resolution; Rural areas; Rural environments; Santé publique et épidémiologie; Satellite imagery; Spaceborne remote sensing; Survival; Vector-borne diseases; Vectors; Warning systems; Burkina Faso; Cross-correlation maps; Anopheles; Statistical modeling; Malaria; Ecological niche; Africa; Interpretable machine learning; Abundance; Random forest; Earth observation data; Biting behavior
• ISSN: 1756-3305; 1756-3305
• DOI: 10.1186/s13071-021-04851-x

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. 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. 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, flight ranges from breeding sites and dispersal related to landscape openness. 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 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).