MATLAB .m file: Code for food chain model that generates simulated baseline data array. Surveillance scenario analysis code also included. No other data or code are required to replicate results from Towards an integrated food safety surveillance system: a simulation study to explore the potential of combining genomic and epidemiological metadata

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. NGS 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 likely more effectively generate insight into foodborne risk than prescribing the hazard in ever more detail in terms of genotype.

食源性感染（foodborne infection）源于暴露于复杂且动态演变的食品系统。食源性感染的传播效率受遗传机制的持续演变所驱动。下一代测序技术（Next Generation Sequencing, NGS）可获取病原体遗传学层面的高保真数据。然而，当前的食品安全监测系统尚无法提供可与遗传数据关联的同等高保真流行病学元数据（epidemiological metadata）。正因如此，将遗传数据转化为可切实指导风险评估或防控食源性疾病暴发的可付诸行动的知识的情形极为罕见。大数据方法被誉为决策支持领域的一场变革，为填补食品安全监测中遗传数据与流行病学元数据之间的保真度差距提供了极具吸引力的潜在路径。为此，我们构建了一款简易食物链模型，以探究整合遗传数据与高保真流行病学元数据等“大数据”来源的潜在价值。研究结果显示，与所有监测系统一样，若要准确认知系统风险，所采集的数据必须具备相关性且能够刻画系统的关键动态——这意味着我们需要审慎开展数据治理（data curation），而非大数据支持者所宣扬的宏大论调：即通过整合非结构化且不相关的数据源即可获取一致的研究洞见。值得关注的是，影响食源性感染风险的最关键因素为污染物负荷与加工温度，而非病原体基因型。这表明，相较于从基因型层面愈发细致地界定危害，明晰食物链动态或能更有效地为食源性风险防控提供研究洞见。