Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26864
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: A model for estimating pathogen variability in shellfish and predicting minimum depuration times
Authors: McMenemy, Paul
Kleczkowski, Adam
Lees, David N
Lowther, James
Taylor, Nicholas G H
Contact Email: paul.mcmenemy@stir.ac.uk
Keywords: shellfish
gastroenteritis
mathematical modelling
log-normal distribution
worst case scenario variability
oyster
Magallana gigas
depuration
norovirus
Escherichia coli
FRNA+ bacteriophage
Issue Date: 7-Mar-2018
Citation: McMenemy P, Kleczkowski A, Lees DN, Lowther J & Taylor NGH (2018) A model for estimating pathogen variability in shellfish and predicting minimum depuration times, PLoS ONE, 13 (3), Art. No.: e0193865.
Abstract: Norovirus 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.
DOI Link: http://dx.doi.org/10.1371/journal.pone.0193865
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.

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