{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T18:43:00Z","timestamp":1792521780928,"version":"4.5.0"},"reference-count":61,"publisher":"eLife Sciences Publications, Ltd","license":[{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"am","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000691","name":"Academy of Medical Sciences","doi-asserted-by":"publisher","award":["SBF005\\1089"],"award-info":[{"award-number":["SBF005\\1089"]}],"id":[{"id":"10.13039\/501100000691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elifesciences.org"],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2023,4,2]]},"abstract":"<jats:p>\n                    <jats:italic>Salmonella enterica<\/jats:italic>\n                    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\n                    <jats:italic>S.<\/jats:italic>\n                    Enteritidis infections from whole genome sequencing data. 2313\n                    <jats:italic>S.<\/jats:italic>\n                    Enteritidis genomes, collected by the UKHSA between 2014\u20132019, were used to train a \u2018local classifier per node\u2019 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: &gt;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 &lt;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.\n                  <\/jats:p>","DOI":"10.7554\/elife.84167","type":"journal-article","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T08:00:26Z","timestamp":1681286426000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.84167","source":"Crossref","is-referenced-by-count":26,"title":["Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning"],"prefix":"10.7554","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5997-2002","authenticated-orcid":true,"given":"Sion C","family":"Bayliss","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0524sp257","id-type":"ROR","asserted-by":"publisher"}],"name":"Bristol Veterinary School, University of Bristol","place":["Bristol, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rebecca K","family":"Locke","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/002h8g185","id-type":"ROR","asserted-by":"publisher"}],"name":"Milner Centre for Evolution, Life Sciences Department, University of Bath","place":["Bath, United Kingdom"]},{"id":[{"id":"https:\/\/ror.org\/04v54gj93","id-type":"ROR","asserted-by":"publisher"}],"name":"Genomic Laboratory Hub (GLH), Addenbrooke\u2019s Hospital, Cambridge University Hospitals NHS Foundation Trust","place":["Cambridge, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claire","family":"Jenkins","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/018h10037","id-type":"ROR","asserted-by":"publisher"}],"name":"Gastrointestinal Reference Services, UK Health Security Agency","place":["London, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marie Anne","family":"Chattaway","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/018h10037","id-type":"ROR","asserted-by":"publisher"}],"name":"Gastrointestinal Reference Services, UK Health Security Agency","place":["London, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy J","family":"Dallman","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04pp8hn57","id-type":"ROR","asserted-by":"publisher"}],"name":"Institute for Risk Assessment Sciences, Utrecht University","place":["Utrecht, Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0835-7971","authenticated-orcid":true,"given":"Lauren A","family":"Cowley","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/002h8g185","id-type":"ROR","asserted-by":"publisher"}],"name":"Milner Centre for Evolution, Life Sciences Department, University of Bath","place":["Bath, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4374","published-online":{"date-parts":[[2023,4,12]]},"reference":[{"key":"bib1","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.copbio.2017.11.002","article-title":"Genomics of foodborne pathogens for microbial food safety","volume":"49","author":"Allard","year":"2018","journal-title":"Current Opinion in Biotechnology"},{"key":"bib2","doi-asserted-by":"publisher","DOI":"10.1099\/mgen.0.000093","article-title":"Microreact: visualizing and sharing data for genomic epidemiology and phylogeography","volume":"2","author":"Argim\u00f3n","year":"2016","journal-title":"Microbial Genomics"},{"key":"bib3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1009436","article-title":"Machine learning to predict the source of campylobacteriosis using whole genome data","volume":"17","author":"Arning","year":"2021","journal-title":"PLOS Genetics"},{"key":"bib4","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.1752","article-title":"Identification of salmonella for public health surveillance using whole genome sequencing","volume":"4","author":"Ashton","year":"2016","journal-title":"PeerJ"},{"key":"bib5","article-title":"Hierarchical machine learning (HML)","volume-title":"Software Heritage","author":"Bayliss","year":"2023","unstructured":"Bayliss S, Cowley L. 2023. 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