Machine Learning Potential for Identifying and Forecasting Complex Environmental Drivers of Vibrio vulnificus Infections in the United States Article Swipe

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Amy Campbell , Jordi Manuel Cabrera-Gumbau , Joaquín Triñanes , Craig Baker‐Austin , Jaime Martínez-Urtaza ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1289/ehp15593

The machine learning approach was able to characterize nonlinear and interacting environmental associations driving V. vulnificus infections. This study accentuates the potential of machine learning and robust surveillance for forecasting environmentally associated marine infections, providing future directions for improvements, further application, and operationalization. https://doi.org/10.1289/EHP15593.

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Vibrio vulnificus Environmental health Vibrio Infections Microbiology Medicine Biology Vibrio Bacteria Genetics

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Type: article
Language: en
Landing Page: https://doi.org/10.1289/ehp15593
OA Status: diamond
Cited By: 3
References: 49
Related Works: 10
OpenAlex ID: https://openalex.org/W4406752319

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OpenAlex ID: https://openalex.org/W4406752319

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DOI: https://doi.org/10.1289/ehp15593

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Title: Machine Learning Potential for Identifying and Forecasting Complex Environmental Drivers of Vibrio vulnificus Infections in the United States

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Type: article

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Language: en

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Publication year: 2025

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Publication date: 2025-01-01

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Authors: Amy Campbell, Jordi Manuel Cabrera-Gumbau, Joaquín Triñanes, Craig Baker‐Austin, Jaime Martínez-Urtaza

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Landing page: https://doi.org/10.1289/ehp15593

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OA status: diamond

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OA URL: https://doi.org/10.1289/ehp15593

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Concepts: Vibrio vulnificus, Environmental health, Vibrio Infections, Microbiology, Medicine, Biology, Vibrio, Bacteria, Genetics

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Cited by: 3

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Citations by year (recent): 2025: 3

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References (count): 49

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Related works (count): 10

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