Machine learning to predict dynamic changes of pathogenic Vibrio spp. abundance on microplastics in marine environment

Abstract

Microplastics are widely found in the marine environment. Recent studies have shown that pathogenic microorganisms can hitchhike on microplastics, which might act as a vector for the spread of pathogens. Vibrio spp. are known to be pathogenic to humans and can cause serious foodborne diseases. In this study, using datasets from an estuary and a mariculture zone in China, five machine learning models were established to predict the relative abundance of Vibrio spp. on microplastics. The results showed that deep neural network (DNN) model and RandomForest algorithm achieved the best predictive performance. Different data sources, data sampling, and processing methods had a little impact on the prediction performance of DNN and RandomForest models. SHapley Additive exPlanations (SHAP) indicated that salinity and temperature are the primary factors affecting the relative abundance of Vibrio spp. The prediction performances of the five machine learning models were further improved by feature selection, providing information to support future experimental research. The results of this study could help establish a long-term and dynamic monitoring system for the relative abundance of Vibrio spp. on microplastics in response to environmental factors as well as provide useful information for assessing the potential health impacts of microplastics on marine ecology and humans.