Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning.

ABSTRACT: Salmonella enterica serovar Enteritidis is one of the most frequent causes of Salmonellosis globally and is commonly transmitted from animals to humans by the consumption of contaminated foodstuffs. In the UK and many other countries in the Global North, a significant proportion of cases are caused by the consumption of imported food products or contracted during foreign travel, therefore, making the rapid identification of the geographical source of new infections a requirement for robust public health outbreak investigations. Herein, we detail the development and application of a hierarchical machine learning model to rapidly identify and trace the geographical source of S. Enteritidis infections from whole genome sequencing data. 2313 S. Enteritidis genomes, collect

SUBMITTER: Bayliss SC 

PROVIDER: S-EPMC10147375 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature