Modeling and Forecasting Vibrio Parahaemolyticus Concentrations in Oysters

• V.p abundance depends on antecedent environmental conditions 1–11 days before The can be described with time-lagged temperature and salinity Four Random Forest-based forecasting models differing lead times were created in oysters forecasted 1–4 advance the enable managers to focus more preventing infections Vibrio parahaemolyticus ( ) is an epidemiologically significant pathogen that thrives coastal waters where are harvested, posing high risks human health shellfish industry requiring effective for emergency preparedness interventions. This study sought develop times, which able predict level of mitigate risk general public economic loss industry. Forest method along 227 sampling datasets from two different geographic locations utilized to: (1) Identify most critical predictors controlling oysters, (2) Select important time lags as model input variables, (3) Develop four (RF-1Day, RF-2Day, RF-3Day, RF-4Day) one days. uncertainty involved predictions was quantified using bootstrapping method. Results showed controlled by before. Sea Surface Temperature (SST) salinity. well 1 – 4 models. performance decreases increasing time. RF-3Day RF-4Day employed primarily due their relatively long while RF-1Day RF-2Day used management interventions predictive performance.