Nobutoshi Nawa, Takeo Fujiwara
Association between social capital and second dose of measles vaccination in Japan: Results from the A-CHILD study
Vaccine
vibrio parahaemolyticus vibrio vulnificus bacteria significant public health impact identifying factors impacting presence concentrations food sources could enable identification significant risk factors prevent incidences foodborne illness recent years machine learning shown promise modeling microbial presence based prevalent external internal variables environmental variables gene presenceabsence respectively particularly generation availability large amounts diverse sources data analyses prove useful predicting microbial behavior food systems particularly influence constant changes environmental variables study tested efficacy six machine learning regression models random forest support vector machine elastic net neural network knearest neighbors extreme gradient boosting predicting relationship environmental variables total pathogenic v parahaemolyticus v vulnificus concentrations seawater oysters general environmental variables found reliable predictors total pathogenic v parahaemolyticus v vulnificus concentrations seawater pathogenic v parahaemolyticus oysters acceptable prediction zone 70 analyzed using machine learning models shapley additive explanations used identify variables influencing vibrio concentrations identified chlorophyll content seawater salinity seawater temperature turbidity influential variables important note different strains differentially impacted environmental variable indicating need research study causes potential mechanisms variations conclusion environmental variables could important predictors vibrio growth behavior seafood moreover models developed study could prove invaluable assessing managing risks associated v parahaemolyticus v vulnificus particularly face changing environment