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Lookup NU author(s): Professor Sarah O'Brien
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Foodborne infection is a result of exposure to complex, dynamic food systems. The efficiency of foodborne infection is driven by ongoing shifts in genetic machinery. Next-generation sequencing technologies can provide high-fidelity data about the genetics of a pathogen. However, food safety surveillance systems do not currently provide similar high-fidelity epidemiological metadata to associate with genetic data. As a consequence, it is rarely possible to transform genetic data into actionable knowledge that can be used to genuinely inform risk assessment or prevent outbreaks. Big data approaches are touted as a revolution in decision support, and pose a potentially attractive method for closing the gap between the fidelity of genetic and epidemiological metadata for food safety surveillance. We therefore developed a simple food chain model to investigate the potential benefits of combining ‘big’ data sources, including both genetic and high-fidelity epidemiological metadata. Our results suggest that, as for any surveillance system, the collected data must be relevant and characterize the important dynamics of a system if we are to properly understand risk: this suggests the need to carefully consider data curation, rather than the more ambitious claims of big data proponents that unstructured and unrelated data sources can be combined to generate consistent insight. Of interest is that the biggest influencers of foodborne infection risk were contamination load and processing temperature, not genotype. This suggests that understanding food chain dynamics would probably more effectively generate insight into foodborne risk than prescribing the hazard in ever more detail in terms of genotype.
Author(s): Hill AA, Crotta M, Wall B, Good L, O'Brien SJ, Guitian J
Publication type: Article
Publication status: Published
Journal: Royal Society Open Science
Year: 2017
Volume: 4
Online publication date: 29/03/2017
Acceptance date: 27/02/2017
Date deposited: 13/08/2019
ISSN (electronic): 2054-5703
Publisher: The Royal Society
URL: https://doi.org/10.1098/rsos.160721
DOI: 10.1098/rsos.160721
PubMed id: 28405360
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