Cargando…
Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning
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...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147375/ https://www.ncbi.nlm.nih.gov/pubmed/37042517 http://dx.doi.org/10.7554/eLife.84167 |
_version_ | 1785034787469131776 |
---|---|
author | Bayliss, Sion C Locke, Rebecca K Jenkins, Claire Chattaway, Marie Anne Dallman, Timothy J Cowley, Lauren A |
author_facet | Bayliss, Sion C Locke, Rebecca K Jenkins, Claire Chattaway, Marie Anne Dallman, Timothy J Cowley, Lauren A |
author_sort | Bayliss, Sion C |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10147375 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-101473752023-04-29 Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning Bayliss, Sion C Locke, Rebecca K Jenkins, Claire Chattaway, Marie Anne Dallman, Timothy J Cowley, Lauren A eLife Epidemiology and Global Health 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. eLife Sciences Publications, Ltd 2023-04-12 /pmc/articles/PMC10147375/ /pubmed/37042517 http://dx.doi.org/10.7554/eLife.84167 Text en © 2023, Bayliss et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Epidemiology and Global Health Bayliss, Sion C Locke, Rebecca K Jenkins, Claire Chattaway, Marie Anne Dallman, Timothy J Cowley, Lauren A Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning |
title | Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning |
title_full | Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning |
title_fullStr | Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning |
title_full_unstemmed | Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning |
title_short | Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning |
title_sort | rapid geographical source attribution of salmonella enterica serovar enteritidis genomes using hierarchical machine learning |
topic | Epidemiology and Global Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147375/ https://www.ncbi.nlm.nih.gov/pubmed/37042517 http://dx.doi.org/10.7554/eLife.84167 |
work_keys_str_mv | AT baylisssionc rapidgeographicalsourceattributionofsalmonellaentericaserovarenteritidisgenomesusinghierarchicalmachinelearning AT lockerebeccak rapidgeographicalsourceattributionofsalmonellaentericaserovarenteritidisgenomesusinghierarchicalmachinelearning AT jenkinsclaire rapidgeographicalsourceattributionofsalmonellaentericaserovarenteritidisgenomesusinghierarchicalmachinelearning AT chattawaymarieanne rapidgeographicalsourceattributionofsalmonellaentericaserovarenteritidisgenomesusinghierarchicalmachinelearning AT dallmantimothyj rapidgeographicalsourceattributionofsalmonellaentericaserovarenteritidisgenomesusinghierarchicalmachinelearning AT cowleylaurena rapidgeographicalsourceattributionofsalmonellaentericaserovarenteritidisgenomesusinghierarchicalmachinelearning |