Abstract
BackgroundImproving 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.MethodsAnopheles 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.ResultsMeteorological 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.ConclusionsUsing 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).Graphical abstract
Highlights
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
Taconet et al Parasites Vectors (2021) 14:345 operational tools to support the fight against the disease
Individuals belonging to the Anopheles gambiae complex and the Anopheles funestus group were identified to species by Polymerase chain reaction (PCR) [31,32,33]
Summary
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. Malaria is a vector-borne disease transmitted by Anopheles mosquitoes still affecting 229 million people and causing more than 400,000 deaths worldwide annually [1]. Mainly through the massive use of long-lasting insecticidal nets [2], led to a sustained decrease of the disease burden between 2000 and 2015 [1]. For effective and sustainable vector control (VC), locally tailored interventions, built on a thorough knowledge of the local determinants of malaria transmission, are needed [3,4,5]. It is of particular importance to decipher with vector bio-ecology at fine and operational spatiotemporal scales [3, 4, 6]
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