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Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations

The utility of characterizing the effects of strain variation and individual/subgroup susceptibility on dose–response outcomes has motivated the search for new approaches beyond the popular use of the exponential dose–response model for listeriosis. While descriptive models can account for such vari...

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Autores principales: Rahman, Ashrafur, Munther, Daniel, Fazil, Aamir, Smith, Ben, Wu, Jianhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6124125/
https://www.ncbi.nlm.nih.gov/pubmed/30225020
http://dx.doi.org/10.1098/rsos.180343
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author Rahman, Ashrafur
Munther, Daniel
Fazil, Aamir
Smith, Ben
Wu, Jianhong
author_facet Rahman, Ashrafur
Munther, Daniel
Fazil, Aamir
Smith, Ben
Wu, Jianhong
author_sort Rahman, Ashrafur
collection PubMed
description The utility of characterizing the effects of strain variation and individual/subgroup susceptibility on dose–response outcomes has motivated the search for new approaches beyond the popular use of the exponential dose–response model for listeriosis. While descriptive models can account for such variation, they have limited power to extrapolate beyond the details of particular outbreaks. By contrast, this study exhibits dose–response relationships from a mechanistic basis, quantifying key biological factors involved in pathogen–host dynamics. An efficient computational algorithm and geometric interpretation of the infection pathway are developed to connect dose–response relationships with the underlying bistable dynamics of the model. Relying on in vitro experiments as well as outbreak data, we estimate plausible parameters for the human context. Despite the presence of uncertainty in such parameters, sensitivity analysis reveals that the host response is most influenced by the pathogen–immune system interaction. In particular, we show how variation in this interaction across a subgroup of the population dictates the shape of dose–response curves. Finally, in terms of future experimentation, our model results provide guidelines and highlight vital aspects of the interplay between immune cells and particular strains of Listeria monocytogenes that should be examined.
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spelling pubmed-61241252018-09-17 Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations Rahman, Ashrafur Munther, Daniel Fazil, Aamir Smith, Ben Wu, Jianhong R Soc Open Sci Mathematics The utility of characterizing the effects of strain variation and individual/subgroup susceptibility on dose–response outcomes has motivated the search for new approaches beyond the popular use of the exponential dose–response model for listeriosis. While descriptive models can account for such variation, they have limited power to extrapolate beyond the details of particular outbreaks. By contrast, this study exhibits dose–response relationships from a mechanistic basis, quantifying key biological factors involved in pathogen–host dynamics. An efficient computational algorithm and geometric interpretation of the infection pathway are developed to connect dose–response relationships with the underlying bistable dynamics of the model. Relying on in vitro experiments as well as outbreak data, we estimate plausible parameters for the human context. Despite the presence of uncertainty in such parameters, sensitivity analysis reveals that the host response is most influenced by the pathogen–immune system interaction. In particular, we show how variation in this interaction across a subgroup of the population dictates the shape of dose–response curves. Finally, in terms of future experimentation, our model results provide guidelines and highlight vital aspects of the interplay between immune cells and particular strains of Listeria monocytogenes that should be examined. The Royal Society Publishing 2018-08-01 /pmc/articles/PMC6124125/ /pubmed/30225020 http://dx.doi.org/10.1098/rsos.180343 Text en © 2018 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Mathematics
Rahman, Ashrafur
Munther, Daniel
Fazil, Aamir
Smith, Ben
Wu, Jianhong
Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations
title Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations
title_full Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations
title_fullStr Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations
title_full_unstemmed Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations
title_short Advancing risk assessment: mechanistic dose–response modelling of Listeria monocytogenes infection in human populations
title_sort advancing risk assessment: mechanistic dose–response modelling of listeria monocytogenes infection in human populations
topic Mathematics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6124125/
https://www.ncbi.nlm.nih.gov/pubmed/30225020
http://dx.doi.org/10.1098/rsos.180343
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