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

This is the authors' abstract. We don't add key points for this paper.

BACKGROUND: in the environment have been well-characterized, fewer models have been able to apply this to human infection risk due to limited surveillance. OBJECTIVES: infections. METHODS: infections based on environmental data. RESULTS: infections. Further models were also developed to explore multilevel spatial resolution, finding state-specific models can improve specificity and early warning system potential by exclusively using lagged environmental data. DISCUSSION: 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.