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利用 SNP 地址进行常规兽医暴发检测中的 DT104。

Using SNP addresses for DT104 in routine veterinary outbreak detection.

机构信息

Department of Epidemiological Sciences, Animal and Plant Health Agency, Weybridge, UK.

Natural Resources Institute, University of Greenwich, Chatham, UK.

出版信息

Epidemiol Infect. 2023 Oct 25;151:e187. doi: 10.1017/S0950268823001723.

Abstract

SNP addresses are a pathogen typing method based on whole-genome sequences (WGSs), assigning groups at seven different levels of genetic similarity. Public health surveillance uses it for several gastro-intestinal infections; this work trialled its use in veterinary surveillance for salmonella outbreak detection. Comparisons were made between temporal and spatio-temporal cluster detection models that either defined cases by their SNP address or by phage type, using historical data sets. Clusters of SNP incidents were effectively detected by both methods, but spatio-temporal models consistently detected these clusters earlier than the corresponding temporal models. Unlike phage type, SNP addresses appeared spatially and temporally limited, which facilitated the differentiation of novel, stable, or expanding clusters in spatio-temporal models. Furthermore, these models flagged spatio-temporal clusters containing only two to three cases at first detection, compared with a median of seven cases in phage-type models. The large number of SNP addresses will require automated methods to implement these detection models routinely. Further work is required to explore how temporal changes and different host species may impact the sensitivity and specificity of cluster detection. In conclusion, given validation with more sequencing data, SNP addresses are likely to be a valuable addition to early warning systems in veterinary surveillance.

摘要

SNP 地址是一种基于全基因组序列(WGS)的病原体分型方法,将具有七种不同遗传相似性水平的群组进行分组。公共卫生监测将其用于多种胃肠感染;本工作尝试将其用于沙门氏菌爆发检测的兽医监测。通过使用历史数据集,比较了通过 SNP 地址或噬菌体型定义病例的时间和时空聚类检测模型。这两种方法都有效地检测到了 SNP 事件的聚类,但时空模型始终比相应的时间模型更早地检测到这些聚类。与噬菌体型不同,SNP 地址在空间和时间上似乎受到限制,这有利于在时空模型中区分新的、稳定的或扩展的聚类。此外,这些模型首先检测到只有两到三个病例的时空聚类,而噬菌体型模型的中位数为七个病例。大量的 SNP 地址将需要自动化方法来定期实施这些检测模型。需要进一步研究时间变化和不同宿主物种如何影响聚类检测的灵敏度和特异性。总之,在经过更多测序数据的验证后,SNP 地址很可能成为兽医监测预警系统的一个有价值的补充。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98f9/10644063/efc0fe0e76eb/S0950268823001723_fig1.jpg

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