Machine Learning Potential for Identifying and Forecasting Complex Environmental Drivers of <i>Vibrio vulnificus</i> Infections in the United States
Amy Campbell, Jordi Manuel Cabrera-Gumbau, Joaquín Triñanes, Craig Baker‐Austin, Jaime Martínez-Urtaza
Abstract
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.