Machine Learning Potential for Identifying and Forecasting Complex Environmental Drivers of <i>Vibrio vulnificus</i> Infections in the United States.

ABSTRACT:

Background

Environmental change in coastal areas can drive marine bacteria and resulting infections, such as those caused by Vibrio vulnificus, with both foodborne and nonfoodborne exposure routes and high mortality. Although ecological drivers of V. vulnificus in the environment have been well-characterized, fewer models have been able to apply this to human infection risk due to limited surveillance.

Objectives

The Cholera and Other Vibrio Illness Surveillance (COVIS) system database has reported V. vulnificus infections in the United States since 1988, offering a unique opportunity to both explore the forecasting capabilities machine learning could provide and to characterize complex environmental drivers of V. vulnificus infections.

SUBMITTER: Campbell AM 

PROVIDER: S-EPMC11756857 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature