Rapid geographical source attribution of <i>Salmonella enterica</i> serovar Enteritidis genomes using hierarchical machine learning.

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.

Bayliss, Sion C; Locke, Rebecca K; Jenkins, Claire; Chattaway, Marie Anne; Dallman, Timothy J; Cowley, Lauren A.

Afiliación

Bayliss SC; Bristol Veterinary School, University of Bristol, Bristol, United Kingdom.
Locke RK; Milner Centre for Evolution, Life Sciences Department, University of Bath, Bath, United Kingdom.
Jenkins C; Genomic Laboratory Hub (GLH), Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.
Chattaway MA; Gastrointestinal Reference Services, UK Health Security Agency, London, United Kingdom.
Dallman TJ; Gastrointestinal Reference Services, UK Health Security Agency, London, United Kingdom.
Cowley LA; Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands.

Elife ; 122023 04 12.

Article en En | MEDLINE | ID: mdl-37042517

Asunto(s)

Infecciones por Salmonella; Salmonella enterica; Animales; Humanos; Salmonella enteritidis/genética; Estudios Prospectivos; Infecciones por Salmonella/epidemiología; Brotes de Enfermedades; Aprendizaje Automático

Palabras clave

Salmonella; epidemiology; gastroenteritis; genomics; global health; infectious disease; machine learning; microbiology; public health

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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Infecciones por Salmonella / Salmonella enterica Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Animals / Humans Idioma: En Revista: Elife Año: 2023 Tipo del documento: Article País de afiliación: Reino Unido

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