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

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Locke, Rebecca K 
Jenkins, Claire 
Chattaway, Marie Anne 
Dallman, Timothy J 


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, collected by the UKHSA between 2014-2019, were used to train a 'local classifier per node' hierarchical classifier to attribute isolates to four continents, 11 sub-regions, and 38 countries (53 classes). The highest classification accuracy was achieved at the continental level followed by the sub-regional and country levels (macro F1: 0.954, 0.718, 0.661, respectively). A number of countries commonly visited by UK travelers were predicted with high accuracy (hF1: >0.9). Longitudinal analysis and validation with publicly accessible international samples indicated that predictions were robust to prospective external datasets. The hierarchical machine learning framework provided granular geographical source prediction directly from sequencing reads in <4 min per sample, facilitating rapid outbreak resolution and real-time genomic epidemiology. The results suggest additional application to a broader range of pathogens and other geographically structured problems, such as antimicrobial resistance prediction, is warranted.


Peer reviewed: True

Acknowledgements: We would like to acknowledge both Dr. Harry Thorpe and Dr. Nicola Coyle who have both previously contributed to the development of scripts that underlie the unitig processing pipeline. This work was funded by an Academy of Medical Sciences Springboard grant (SBF005\1089). CJ, TD, and MAC are affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Gastrointestinal Infections and Genomics and Enabling Data at the University of Liverpool and University of Warwick, respectively in partnership with the UK Health Security Agency (UKHSA). CJ and MAC are based at UKHSA. The views expressed are those of the author(s) and not necessarily those of the NIHR, the Department of Health and Social Care, or the UK Health Security Agency.


Salmonella, epidemiology, gastroenteritis, genomics, global health, infectious disease, machine learning, microbiology, public health, Animals, Humans, Salmonella enteritidis, Prospective Studies, Salmonella Infections, Disease Outbreaks, Machine Learning, Salmonella enterica

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eLife Sciences Publications, Ltd
Academy of Medical Sciences (SBF005\1089)