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Towards an integrated food safety surveillance system: a simulation study to explore the potential of combining genomic and epidemiological metadata

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Chronology of the analytical framework. A simulated data array, containing relevant epidemiological and genomic data, was generated from a simple food chain model. We then assumed various rows and columns of the data array could be observed with progressively more advanced surveillance systems. We then interrogated the subsets of the data array (representing different surveillance strategies) to determine the relationships and trends observable by first accessing detailed WGS data alone, and then adding the ability to collect detailed epidemiological data via tracking and tracing systems.
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RSOS160721F1: Chronology of the analytical framework. A simulated data array, containing relevant epidemiological and genomic data, was generated from a simple food chain model. We then assumed various rows and columns of the data array could be observed with progressively more advanced surveillance systems. We then interrogated the subsets of the data array (representing different surveillance strategies) to determine the relationships and trends observable by first accessing detailed WGS data alone, and then adding the ability to collect detailed epidemiological data via tracking and tracing systems.

Mentions: The chronology of the analytical framework was to generate a simulated data array from which to test progressively more advanced surveillance systems (figureĀ 1). The main steps were to:


Towards an integrated food safety surveillance system: a simulation study to explore the potential of combining genomic and epidemiological metadata
Chronology of the analytical framework. A simulated data array, containing relevant epidemiological and genomic data, was generated from a simple food chain model. We then assumed various rows and columns of the data array could be observed with progressively more advanced surveillance systems. We then interrogated the subsets of the data array (representing different surveillance strategies) to determine the relationships and trends observable by first accessing detailed WGS data alone, and then adding the ability to collect detailed epidemiological data via tracking and tracing systems.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5383817&req=5

RSOS160721F1: Chronology of the analytical framework. A simulated data array, containing relevant epidemiological and genomic data, was generated from a simple food chain model. We then assumed various rows and columns of the data array could be observed with progressively more advanced surveillance systems. We then interrogated the subsets of the data array (representing different surveillance strategies) to determine the relationships and trends observable by first accessing detailed WGS data alone, and then adding the ability to collect detailed epidemiological data via tracking and tracing systems.
Mentions: The chronology of the analytical framework was to generate a simulated data array from which to test progressively more advanced surveillance systems (figureĀ 1). The main steps were to:

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.