Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26864
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dc.contributor.authorMcMenemy, Paulen_UK
dc.contributor.authorKleczkowski, Adamen_UK
dc.contributor.authorLees, David Nen_UK
dc.contributor.authorLowther, Jamesen_UK
dc.contributor.authorTaylor, Nicken_UK
dc.date.accessioned2018-04-06T23:48:31Z-
dc.date.available2018-04-06T23:48:31Z-
dc.date.issued2018-03-07en_UK
dc.identifier.othere0193865en_UK
dc.identifier.urihttp://hdl.handle.net/1893/26864-
dc.description.abstractNorovirus is a major cause of viral gastroenteritis, with shellfish consumption being identified as one potential norovirus entry point into the human population. Minimising shellfish norovirus levels is therefore important for both the consumer’s protection and the shellfish industry’s reputation. One method used to reduce microbiological risks in shellfish is depuration; however, this process also presents additional costs to industry. Providing a mechanism to estimate norovirus levels during depuration would therefore be useful to stakeholders. This paper presents a mathematical model of the depuration process and its impact on norovirus levels found in shellfish. Two fundamental stages of norovirus depuration are considered: (i) the initial distribution of norovirus loads within a shellfish population and (ii) the way in which the initial norovirus loads evolve during depuration. Realistic assumptions are made about the dynamics of norovirus during depuration, and mathematical descriptions of both stages are derived and combined into a single model. Parameters to describe the depuration effect and norovirus load values are derived from existing norovirus data obtained from U.K. harvest sites. However, obtaining population estimates of norovirus variability is time-consuming and expensive; this model addresses the issue by assuming a ‘worst case scenario’ for variability of pathogens, which is independent of mean pathogen levels. The model is then used to predict minimum depuration times required to achieve norovirus levels which fall within possible risk management levels, as well as predictions of minimum depuration times for other water-borne pathogens found in shellfish. Times for Escherichia coli predicted by the model all fall within the minimum 42 hours required for class B harvest sites, whereas minimum depuration times for norovirus and FRNA+ bacteriophage are substantially longer. Thus this study provides relevant information and tools to assist norovirus risk managers with future control strategies.en_UK
dc.language.isoenen_UK
dc.publisherPublic Library of Scienceen_UK
dc.relationMcMenemy P, Kleczkowski A, Lees DN, Lowther J & Taylor N (2018) A model for estimating pathogen variability in shellfish and predicting minimum depuration times, PLoS ONE, 13 (3), Art. No.: e0193865. https://doi.org/10.1371/journal.pone.0193865.en_UK
dc.rights© 2018 McMenemy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_UK
dc.subjectshellfishen_UK
dc.subjectgastroenteritisen_UK
dc.subjectmathematical modellingen_UK
dc.subjectlog-normal distributionen_UK
dc.subjectworst case scenario variabilityen_UK
dc.subjectoysteren_UK
dc.subjectMagallana gigasen_UK
dc.subjectdepurationen_UK
dc.subjectnorovirusen_UK
dc.subjectEscherichia colien_UK
dc.subjectFRNA+ bacteriophageen_UK
dc.titleA model for estimating pathogen variability in shellfish and predicting minimum depuration timesen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1371/journal.pone.0193865en_UK
dc.identifier.pmid29513747en_UK
dc.citation.jtitlePLoS ONEen_UK
dc.citation.issn1932-6203en_UK
dc.citation.volume13en_UK
dc.citation.issue3en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderUniversity of Stirlingen_UK
dc.author.emailpaul.mcmenemy@stir.ac.uken_UK
dc.citation.date07/03/2018en_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationMathematicsen_UK
dc.contributor.affiliationCentre for Environment, Fisheries and Aquaculture Science (CEFAS)en_UK
dc.contributor.affiliationCentre for Environment, Fisheries and Aquaculture Science (CEFAS)en_UK
dc.contributor.affiliationCentre for Environment, Fisheries and Aquaculture Science (CEFAS)en_UK
dc.identifier.isi000426896800083en_UK
dc.identifier.scopusid2-s2.0-85042919354en_UK
dc.identifier.wtid494795en_UK
dc.contributor.orcid0000-0002-5280-425Xen_UK
dc.contributor.orcid0000-0003-1384-4352en_UK
dc.date.accepted2018-02-20en_UK
dc.date.firstcompliantdepositdate2018-03-16en_UK
dc.description.refREF Compliant by Deposit in Stirling's Repositoryen_UK
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