A Markov chain model to investigate the spread of antibiotic-resistant bacteria in hospitals

This paper proposes a Markov chain model to describe the spread of a single bacterial species in a hospital ward where patients may be free of bacteria or may carry bacterial strains that are either sensitive or resistant to antimicrobial agents. The aim is to determine the probability law of the ex...

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Detalhes bibliográficos
Autores: Chalub, Fabio A.C.C., Gómez-Corral, Antonio, López-García, Martín, Palacios-Rodríguez, Fátima
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/72422
Acesso em linha:https://hdl.handle.net/20.500.14352/72422
Access Level:acceso abierto
Palavra-chave:519.217
Epidemic model
Markov chain
Quasi-birth-death process
Reproduction number
Investigación operativa (Matemáticas)
Procesos estocásticos
Biomatemáticas
1207 Investigación Operativa
1208.08 Procesos Estocásticos
2404 Biomatemáticas
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oai_identifier_str oai:docta.ucm.es:20.500.14352/72422
network_acronym_str ES
network_name_str España
repository_id_str
spelling A Markov chain model to investigate the spread of antibiotic-resistant bacteria in hospitalsChalub, Fabio A.C.C.Gómez-Corral, AntonioLópez-García, MartínPalacios-Rodríguez, Fátima519.217Epidemic modelMarkov chainQuasi-birth-death processReproduction numberInvestigación operativa (Matemáticas)Procesos estocásticosBiomatemáticas1207 Investigación Operativa1208.08 Procesos Estocásticos2404 BiomatemáticasThis paper proposes a Markov chain model to describe the spread of a single bacterial species in a hospital ward where patients may be free of bacteria or may carry bacterial strains that are either sensitive or resistant to antimicrobial agents. The aim is to determine the probability law of the exact reproduction number Rexact,0 which is here defined as the random number of secondary infections generated by those patients who are accommodated in a predetermined bed before a patient who is free of bacteria is accommodated in this bed for the first time. Specifically, we decompose the exact reproduction number Rexact,0 into two contributions allowing us to distinguish between infections due to the sensitive and the resistant bacterial strains. Our methodology is mainly based on structured Markov chains and the use of related matrix-analytic methods.Universidad Complutense de Madrid20232023-05-2420232023-05-24journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/72422reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución 3.0 Españahttps://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/724222026-06-02T12:44:21Z
dc.title.none.fl_str_mv A Markov chain model to investigate the spread of antibiotic-resistant bacteria in hospitals
title A Markov chain model to investigate the spread of antibiotic-resistant bacteria in hospitals
spellingShingle A Markov chain model to investigate the spread of antibiotic-resistant bacteria in hospitals
Chalub, Fabio A.C.C.
519.217
Epidemic model
Markov chain
Quasi-birth-death process
Reproduction number
Investigación operativa (Matemáticas)
Procesos estocásticos
Biomatemáticas
1207 Investigación Operativa
1208.08 Procesos Estocásticos
2404 Biomatemáticas
title_short A Markov chain model to investigate the spread of antibiotic-resistant bacteria in hospitals
title_full A Markov chain model to investigate the spread of antibiotic-resistant bacteria in hospitals
title_fullStr A Markov chain model to investigate the spread of antibiotic-resistant bacteria in hospitals
title_full_unstemmed A Markov chain model to investigate the spread of antibiotic-resistant bacteria in hospitals
title_sort A Markov chain model to investigate the spread of antibiotic-resistant bacteria in hospitals
dc.creator.none.fl_str_mv Chalub, Fabio A.C.C.
Gómez-Corral, Antonio
López-García, Martín
Palacios-Rodríguez, Fátima
author Chalub, Fabio A.C.C.
author_facet Chalub, Fabio A.C.C.
Gómez-Corral, Antonio
López-García, Martín
Palacios-Rodríguez, Fátima
author_role author
author2 Gómez-Corral, Antonio
López-García, Martín
Palacios-Rodríguez, Fátima
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 519.217
Epidemic model
Markov chain
Quasi-birth-death process
Reproduction number
Investigación operativa (Matemáticas)
Procesos estocásticos
Biomatemáticas
1207 Investigación Operativa
1208.08 Procesos Estocásticos
2404 Biomatemáticas
topic 519.217
Epidemic model
Markov chain
Quasi-birth-death process
Reproduction number
Investigación operativa (Matemáticas)
Procesos estocásticos
Biomatemáticas
1207 Investigación Operativa
1208.08 Procesos Estocásticos
2404 Biomatemáticas
description This paper proposes a Markov chain model to describe the spread of a single bacterial species in a hospital ward where patients may be free of bacteria or may carry bacterial strains that are either sensitive or resistant to antimicrobial agents. The aim is to determine the probability law of the exact reproduction number Rexact,0 which is here defined as the random number of secondary infections generated by those patients who are accommodated in a predetermined bed before a patient who is free of bacteria is accommodated in this bed for the first time. Specifically, we decompose the exact reproduction number Rexact,0 into two contributions allowing us to distinguish between infections due to the sensitive and the resistant bacterial strains. Our methodology is mainly based on structured Markov chains and the use of related matrix-analytic methods.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-05-24
2023
2023-05-24
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/72422
url https://hdl.handle.net/20.500.14352/72422
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución 3.0 España
https://creativecommons.org/licenses/by/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución 3.0 España
https://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,198674

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