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Can biosecurity and local network properties predict pathogen species richness in the salmonid industry?

Salmonid farming in Ireland is mostly organic, which implies limited disease treatment options. This highlights the importance of biosecurity for preventing the introduction and spread of infectious agents. Similarly, the effect of local network properties on infection spread processes has rarely be...

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Autores principales: Yatabe, Tadaishi, More, Simon J., Geoghegan, Fiona, McManus, Catherine, Hill, Ashley E., Martínez-López, Beatriz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5790274/
https://www.ncbi.nlm.nih.gov/pubmed/29381760
http://dx.doi.org/10.1371/journal.pone.0191680
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author Yatabe, Tadaishi
More, Simon J.
Geoghegan, Fiona
McManus, Catherine
Hill, Ashley E.
Martínez-López, Beatriz
author_facet Yatabe, Tadaishi
More, Simon J.
Geoghegan, Fiona
McManus, Catherine
Hill, Ashley E.
Martínez-López, Beatriz
author_sort Yatabe, Tadaishi
collection PubMed
description Salmonid farming in Ireland is mostly organic, which implies limited disease treatment options. This highlights the importance of biosecurity for preventing the introduction and spread of infectious agents. Similarly, the effect of local network properties on infection spread processes has rarely been evaluated. In this paper, we characterized the biosecurity of salmonid farms in Ireland using a survey, and then developed a score for benchmarking the disease risk of salmonid farms. The usefulness and validity of this score, together with farm indegree (dichotomized as ≤ 1 or > 1), were assessed through generalized Poisson regression models, in which the modeled outcome was pathogen richness, defined here as the number of different diseases affecting a farm during a year. Seawater salmon (SW salmon) farms had the highest biosecurity scores with a median (interquartile range) of 82.3 (5.4), followed by freshwater salmon (FW salmon) with 75.2 (8.2), and freshwater trout (FW trout) farms with 74.8 (4.5). For FW salmon and trout farms, the top ranked model (in terms of leave-one-out information criteria, looic) was the null model (looic = 46.1). For SW salmon farms, the best ranking model was the full model with both predictors and their interaction (looic = 33.3). Farms with a higher biosecurity score were associated with lower pathogen richness, and farms with indegree > 1 (i.e. more than one fish supplier) were associated with increased pathogen richness. The effect of the interaction between these variables was also important, showing an antagonistic effect. This would indicate that biosecurity effectiveness is achieved through a broader perspective on the subject, which includes a minimization in the number of suppliers and hence in the possibilities for infection to enter a farm. The work presented here could be used to elaborate indicators of a farm’s disease risk based on its biosecurity score and indegree, to inform risk-based disease surveillance and control strategies for private and public stakeholders.
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spelling pubmed-57902742018-02-13 Can biosecurity and local network properties predict pathogen species richness in the salmonid industry? Yatabe, Tadaishi More, Simon J. Geoghegan, Fiona McManus, Catherine Hill, Ashley E. Martínez-López, Beatriz PLoS One Research Article Salmonid farming in Ireland is mostly organic, which implies limited disease treatment options. This highlights the importance of biosecurity for preventing the introduction and spread of infectious agents. Similarly, the effect of local network properties on infection spread processes has rarely been evaluated. In this paper, we characterized the biosecurity of salmonid farms in Ireland using a survey, and then developed a score for benchmarking the disease risk of salmonid farms. The usefulness and validity of this score, together with farm indegree (dichotomized as ≤ 1 or > 1), were assessed through generalized Poisson regression models, in which the modeled outcome was pathogen richness, defined here as the number of different diseases affecting a farm during a year. Seawater salmon (SW salmon) farms had the highest biosecurity scores with a median (interquartile range) of 82.3 (5.4), followed by freshwater salmon (FW salmon) with 75.2 (8.2), and freshwater trout (FW trout) farms with 74.8 (4.5). For FW salmon and trout farms, the top ranked model (in terms of leave-one-out information criteria, looic) was the null model (looic = 46.1). For SW salmon farms, the best ranking model was the full model with both predictors and their interaction (looic = 33.3). Farms with a higher biosecurity score were associated with lower pathogen richness, and farms with indegree > 1 (i.e. more than one fish supplier) were associated with increased pathogen richness. The effect of the interaction between these variables was also important, showing an antagonistic effect. This would indicate that biosecurity effectiveness is achieved through a broader perspective on the subject, which includes a minimization in the number of suppliers and hence in the possibilities for infection to enter a farm. The work presented here could be used to elaborate indicators of a farm’s disease risk based on its biosecurity score and indegree, to inform risk-based disease surveillance and control strategies for private and public stakeholders. Public Library of Science 2018-01-30 /pmc/articles/PMC5790274/ /pubmed/29381760 http://dx.doi.org/10.1371/journal.pone.0191680 Text en © 2018 Yatabe et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Yatabe, Tadaishi
More, Simon J.
Geoghegan, Fiona
McManus, Catherine
Hill, Ashley E.
Martínez-López, Beatriz
Can biosecurity and local network properties predict pathogen species richness in the salmonid industry?
title Can biosecurity and local network properties predict pathogen species richness in the salmonid industry?
title_full Can biosecurity and local network properties predict pathogen species richness in the salmonid industry?
title_fullStr Can biosecurity and local network properties predict pathogen species richness in the salmonid industry?
title_full_unstemmed Can biosecurity and local network properties predict pathogen species richness in the salmonid industry?
title_short Can biosecurity and local network properties predict pathogen species richness in the salmonid industry?
title_sort can biosecurity and local network properties predict pathogen species richness in the salmonid industry?
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5790274/
https://www.ncbi.nlm.nih.gov/pubmed/29381760
http://dx.doi.org/10.1371/journal.pone.0191680
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