Brinch Maja Lykke, Hald Tine, Wainaina Lynda, Merlotti Alessandra, Remondini Daniel, Henri Clementine, Njage Patrick Murigu Kamau
Research Group for Foodborne Pathogens and Epidemiology, National Food Institute, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.
Department of Mathematics, University of Padova, 35121 Padova, Italy.
Pathogens. 2023 May 31;12(6):786. doi: 10.3390/pathogens12060786.
spp. are the most common cause of bacterial gastrointestinal infection in humans both in Denmark and worldwide. Studies have found microbial subtyping to be a powerful tool for source attribution, but comparisons of different methodologies are limited. In this study, we compare three source attribution approaches (Machine Learning, Network Analysis, and Bayesian modeling) using three types of whole genome sequences (WGS) data inputs (cgMLST, 5-Mers and 7-Mers). We predicted and compared the sources of human campylobacteriosis cases in Denmark. Using 7mer as an input feature provided the best model performance. The network analysis algorithm had a CSC value of 78.99% and an F1-score value of 67%, while the machine-learning algorithm showed the highest accuracy (98%). The models attributed between 965 and all of the 1224 human cases to a source (network applying 5mer and machine learning applying 7mer, respectively). Chicken from Denmark was the primary source of human campylobacteriosis with an average percentage probability of attribution of 45.8% to 65.4%, representing Bayesian with 7mer and machine learning with cgMLST, respectively. Our results indicate that the different source attribution methodologies based on WGS have great potential for the surveillance and source tracking of Campylobacter. The results of such models may support decision makers to prioritize and target interventions.
弯曲杆菌属是丹麦和全球范围内人类细菌性胃肠道感染的最常见病因。研究发现微生物亚型分型是一种强大的溯源工具，但不同方法的比较有限。在本研究中，我们使用三种类型的全基因组序列（WGS）数据输入（核心多位点序列分型（cgMLST）、五聚体和七聚体）比较了三种溯源方法（机器学习、网络分析和贝叶斯建模）。我们预测并比较了丹麦人类弯曲杆菌病病例的来源。使用七聚体作为输入特征可提供最佳的模型性能。网络分析算法的共特异性系数（CSC）值为78.99%，F1分数值为67%，而机器学习算法显示出最高的准确率（98%）。这些模型将965例至全部1224例人类病例归因于一个来源（分别是应用五聚体的网络分析和应用七聚体的机器学习）。丹麦鸡肉是人类弯曲杆菌病的主要来源，归因的平均概率百分比为45.8%至65.4%，分别代表应用七聚体的贝叶斯建模和应用cgMLST的机器学习。我们的结果表明，基于WGS的不同溯源方法在弯曲杆菌监测和来源追踪方面具有巨大潜力。此类模型的结果可能支持决策者对干预措施进行优先排序和确定目标。
