Development of Artificial Intelligence Approach to Nowcasting and Forecasting Oyster Norovirus Outbreaks along the U.S. Gulf Coast

Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide. This study presents an Artificial Intelligence (AI)-based approach to identifying the primary cause of oyster norovirus outbreaks, nowcasting and forecasting the growing risk of oyster norovirus outbreaks in coastal waters. AI models were developed using Artificial Neural Networks (ANNs) and Genetic Programming (GP) methods and time series of epidemiological and environmental data. Input variable selection techniques, including Random Forests (RF) and Forwards Binary Logistic Regression (FBLR), were used to identify the significant model input variables among six independent environmental predictors including water temperature, solar radiation, gage height, salinity, wind, and rainfall and various combinations of the variables with different time lags. In terms of nowcasting, a risk-based GP model was developed to nowcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast, showing the true positive and negative rates of 78.53% and 88.82%, respectively.