A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms
Abstract Antimicrobial resistance (AMR) is one of the major challenges of the century and should be addressed with a One Health approach. This study aimed to develop a tool that can provide a better understanding of AMR patterns and improve management practices in swine production systems to reduce...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-29980-4 |
| _version_ | 1797864729008406528 |
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| author | Cecilia Aguilar-Vega Caterina Scoglio María J. Clavijo Rebecca Robbins Locke Karriker Xin Liu Beatriz Martínez-López |
| author_facet | Cecilia Aguilar-Vega Caterina Scoglio María J. Clavijo Rebecca Robbins Locke Karriker Xin Liu Beatriz Martínez-López |
| author_sort | Cecilia Aguilar-Vega |
| collection | DOAJ |
| description | Abstract Antimicrobial resistance (AMR) is one of the major challenges of the century and should be addressed with a One Health approach. This study aimed to develop a tool that can provide a better understanding of AMR patterns and improve management practices in swine production systems to reduce its spread between farms. We generated similarity networks based on the phenotypic AMR pattern for each farm with information on important bacterial pathogens for swine farming based on the Euclidean distance. We included seven pathogens: Actinobacillus suis, Bordetella bronchiseptica, Escherichia coli, Glaesserella parasuis, Pasteurella multocida, Salmonella spp., and Streptococcus suis; and up to seventeen antibiotics from ten classes. A threshold criterion was developed to reduce the density of the networks and generate communities based on their AMR profiles. A total of 479 farms were included in the study although not all bacteria information was available on each farm. We observed significant differences in the morphology, number of nodes and characteristics of pathogen networks, as well as in the number of communities and susceptibility profiles of the pathogens to different antimicrobial drugs. The methodology presented here could be a useful tool to improve health management, biosecurity measures and prioritize interventions to reduce AMR spread in swine farming. |
| first_indexed | 2024-04-09T22:56:47Z |
| format | Article |
| id | doaj.art-ebe89be85f894a4ba966ec4e4497afb6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T22:56:47Z |
| publishDate | 2023-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-ebe89be85f894a4ba966ec4e4497afb62023-03-22T11:13:48ZengNature PortfolioScientific Reports2045-23222023-02-0113111110.1038/s41598-023-29980-4A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farmsCecilia Aguilar-Vega0Caterina Scoglio1María J. Clavijo2Rebecca Robbins3Locke Karriker4Xin Liu5Beatriz Martínez-López6Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine and Epidemiology, School of Veterinary Medicine, University of CaliforniaDepartment of Electrical and Computer Engineering, Kansas State UniversityDepartment of Veterinary Diagnostic and Production Animal Medicine, Iowa State UniversityPig Improvement Company (PIC)Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State UniversityComputer Science Department, University of CaliforniaCenter for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine and Epidemiology, School of Veterinary Medicine, University of CaliforniaAbstract Antimicrobial resistance (AMR) is one of the major challenges of the century and should be addressed with a One Health approach. This study aimed to develop a tool that can provide a better understanding of AMR patterns and improve management practices in swine production systems to reduce its spread between farms. We generated similarity networks based on the phenotypic AMR pattern for each farm with information on important bacterial pathogens for swine farming based on the Euclidean distance. We included seven pathogens: Actinobacillus suis, Bordetella bronchiseptica, Escherichia coli, Glaesserella parasuis, Pasteurella multocida, Salmonella spp., and Streptococcus suis; and up to seventeen antibiotics from ten classes. A threshold criterion was developed to reduce the density of the networks and generate communities based on their AMR profiles. A total of 479 farms were included in the study although not all bacteria information was available on each farm. We observed significant differences in the morphology, number of nodes and characteristics of pathogen networks, as well as in the number of communities and susceptibility profiles of the pathogens to different antimicrobial drugs. The methodology presented here could be a useful tool to improve health management, biosecurity measures and prioritize interventions to reduce AMR spread in swine farming.https://doi.org/10.1038/s41598-023-29980-4 |
| spellingShingle | Cecilia Aguilar-Vega Caterina Scoglio María J. Clavijo Rebecca Robbins Locke Karriker Xin Liu Beatriz Martínez-López A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms Scientific Reports |
| title | A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms |
| title_full | A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms |
| title_fullStr | A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms |
| title_full_unstemmed | A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms |
| title_short | A tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms |
| title_sort | tool to enhance antimicrobial stewardship using similarity networks to identify antimicrobial resistance patterns across farms |
| url | https://doi.org/10.1038/s41598-023-29980-4 |
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