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

التفاصيل البيبلوغرافية
العنوان: Data-driven and interpretable machine-learning modeling to explore the fine-scale environmental determinants of malaria vectors biting rates in rural Burkina Faso
المؤلفون: Nicolas Moiroux, A. A. Koffi, Karine Mouline, Dieudonné Diloma Soma, Cédric Pennetier, Frédéric Simard, Morgan Mangeas, Angélique Porciani, Paul Taconet, Roch K. Dabiré
المساهمون: Vector Control Group (MIVEGEC-VCG), Evolution des Systèmes Vectoriels (ESV), Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut de Recherche en Sciences de la Santé (IRSS), CNRST, Diversity, ecology, evolution & Adaptation of arthropod vectors (MIVEGEC-DEEVA), Institut Pierre Richet (IPR), Ecologie marine tropicale des océans Pacifique et Indien (ENTROPIE [Nouvelle-Calédonie]), Ifremer - Nouvelle-Calédonie, Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie])-Université de la Nouvelle-Calédonie (UNC), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université de la Nouvelle-Calédonie (UNC)-Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie]), Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie])-Ifremer - Nouvelle-Calédonie, Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de la Nouvelle-Calédonie (UNC)
المصدر: Parasites & Vectors
Parasites & Vectors, 2021, 14, pp.345. ⟨10.1186/s13071-021-04851-x⟩
Parasites & Vectors, Vol 14, Iss 1, Pp 1-23 (2021)
بيانات النشر: Cold Spring Harbor Laboratory, 2021.
سنة النشر: 2021
مصطلحات موضوعية: Rural Population, 0301 basic medicine, Multivariate statistics, Mosquito Control, Anopheles gambiae, Ecological niche, Infectious and parasitic diseases, RC109-216, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning, 0302 clinical medicine, Abundance, Satellite imagery, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, 2. Zero hunger, 0303 health sciences, Interpretable machine learning, biology, Anopheles, Statistical modeling, 3. Good health, Infectious Diseases, Geography, Biting behavior, Seasons, Cartography, Earth observation data, 030231 tropical medicine, Mosquito Vectors, Environment, Machine learning, 03 medical and health sciences, parasitic diseases, Burkina Faso, medicine, Animals, Humans, 030304 developmental biology, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, business.industry, Research, Insect Bites and Stings, 15. Life on land, medicine.disease, biology.organism_classification, Malaria, [SDV.BA.ZI]Life Sciences [q-bio]/Animal biology/Invertebrate Zoology, 030104 developmental biology, Biting, Vector (epidemiology), Africa, Biological dispersal, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Parasitology, Artificial intelligence, Cross-correlation maps, business, Scale (map), computer, Random forest
الوصف: Background 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, flight 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 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
تدمد: 1756-3305
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67153878836236d2ab757fabd42aff70
https://doi.org/10.1101/2021.04.13.439583
حقوق: OPEN
رقم الأكسشن: edsair.doi.dedup.....67153878836236d2ab757fabd42aff70
قاعدة البيانات: OpenAIRE
الوصف
تدمد:17563305