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

Abstract

Background: Improving the knowledge and understanding of the environmental determinants of malaria vectors abundances at fine spatiotemporal scales is essential to design locally tailored vector control intervention. This work 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 Diebougou, 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, both for meteorological and landscape variables. From these results, several aspects of the bio-ecology of the main malaria vectors were precised or hypothesized for the Diebougou area, including breeding sites typologies, development and survival rates in relation to weather, flight ranges from breeding sites, 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 field of interpretable machine-learning has significant potential to help improving our understanding of the complex processes leading to malaria transmission.

A meta-epidemiological assessment of transparency indicators of infectious disease models

“…Of these, 24/29 (82.8%) actually shared code, 29/33 (87.9%) actually shared data and 5/6 (83.3%) were indeed registered. The papers that used registration were two malaria models(14,15), one vector model (16) (which focused on malaria vectors) one polio (Sabin 2 virus (17)) model and one rotavirus model(18). The majority were from 2021(14,16,17) and were also malaria models (two malaria and one vector that was essentially malaria (14–16)) the majority we also classified as spatiotemporal (14–16).…”

“…The papers that used registration were two malaria models(14,15), one vector model (16) (which focused on malaria vectors) one polio (Sabin 2 virus (17)) model and one rotavirus model(18). The majority were from 2021(14,16,17) and were also malaria models (two malaria and one vector that was essentially malaria (14–16)) the majority we also classified as spatiotemporal (14–16). Finally, of the 120 articles (10%) that text mining found that they contained a COI statements, there was indeed a placeholder for this ststement in all articles, but the vast majority of the statements (115 (95.8%)) disclosed no conflict at all.…”

“…The papers that used registration were two malaria models (14,15), one vector model (16) (which focused on malaria vectors) one polio (Sabin 2 virus ( 17)) model and one rotavirus model (18).…”

An illustration of model agnostic explainability methods applied to environmental data

“…LIME has only recently started to be used in environmental and ecological applications (e.g., Cha et al, 2021; Ryo et al, 2021; Taconet et al, 2021).…”