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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.


Related in: MedlinePlus

Change in overall weekly reporting if one random genotype gains efficiency to cause human infection, increasing the probability of infection from one organism by 10×–100×. Because the signal from the one genotype is swamped amongst many other genotypes, it needs a huge increase in infection efficiency (100×) to consistently breach the exceedance score.
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RSOS160721F8: Change in overall weekly reporting if one random genotype gains efficiency to cause human infection, increasing the probability of infection from one organism by 10×–100×. Because the signal from the one genotype is swamped amongst many other genotypes, it needs a huge increase in infection efficiency (100×) to consistently breach the exceedance score.

Mentions: One genotype gaining greater 10× human infection efficiency was unlikely to register within overall weekly reporting of cases; however, an increase in 100× may be notable (figure 8). For example, a genotype with 100× efficiency would make that genotype around 10–100× the efficiency of the previous maximal efficiency genotype. More detailed analysis by the responsible authorities may well detect an anomaly, even if an outbreak alarm is not triggered owing to the relatively low increase in overall cases. However, it is likely that this anomaly may be just one of many, potentially swamping any outbreak signal with noise.Figure 8.


Towards an integrated food safety surveillance system: a simulation study to explore the potential of combining genomic and epidemiological metadata
Change in overall weekly reporting if one random genotype gains efficiency to cause human infection, increasing the probability of infection from one organism by 10×–100×. Because the signal from the one genotype is swamped amongst many other genotypes, it needs a huge increase in infection efficiency (100×) to consistently breach the exceedance score.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSOS160721F8: Change in overall weekly reporting if one random genotype gains efficiency to cause human infection, increasing the probability of infection from one organism by 10×–100×. Because the signal from the one genotype is swamped amongst many other genotypes, it needs a huge increase in infection efficiency (100×) to consistently breach the exceedance score.
Mentions: One genotype gaining greater 10× human infection efficiency was unlikely to register within overall weekly reporting of cases; however, an increase in 100× may be notable (figure 8). For example, a genotype with 100× efficiency would make that genotype around 10–100× the efficiency of the previous maximal efficiency genotype. More detailed analysis by the responsible authorities may well detect an anomaly, even if an outbreak alarm is not triggered owing to the relatively low increase in overall cases. However, it is likely that this anomaly may be just one of many, potentially swamping any outbreak signal with noise.Figure 8.

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.


Related in: MedlinePlus