Document Type
Article
Publication Date
8-1-2018
Publication Title
Royal Society Open Science
Abstract
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. 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.
Repository Citation
Rahman, Ashrafur; Munther, Daniel; Fazil, Aamir; Smith, Ben; and Wu, Jianhong, "Advancing Risk Assessment: Mechanistic Dose-response Modelling of Listeria monocytogenes Infection in Human Populations" (2018). Mathematics and Statistics Faculty Publications. 321.
https://engagedscholarship.csuohio.edu/scimath_facpub/321
DOI
10.1098/rsos.180343
Version
Publisher's PDF
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume
5
Issue
8
Comments
This study is supported by Public Health Agency of Canada. D.M. acknowledges support from ClevelandState University start-up funding (STARTUP42). A.R.’s postdoctoral fellowship is supported by a research contractfrom the Public Health Agency of Canada and by the NSERC CREATE project Advanced Disaster, Emergency andRapid Response Simulations. J.W.’s research has been funded by the Natural Sciences and Engineering ResearchCouncil of Canada and by the Canada Research Chairs program.