Machine learning to predict foodborne salmonellosis outbreaks based on genome characteristics and meteorological trends.

ABSTRACT: Several studies have shown a correlation between outbreaks of Salmonella enterica and meteorological trends, especially related to temperature and precipitation. Additionally, current studies based on outbreaks are performed on data for the species Salmonella enterica, without considering its intra-species and genetic heterogeneity. In this study, we analyzed the effect of differential gene expression and a suite of meteorological factors on salmonellosis outbreak scale (typified by case numbers) using a combination of machine learning and count-based modeling methods. Elastic Net regularization model was used to identify significant genes from a Salmonella pan-genome, and a multi-variable Poisson regression developed to fit the individual and mixed effects data. The b

SUBMITTER: Karanth S 

PROVIDER: S-EPMC10290999 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature