Rose Erica Billig, Steele Molly K, Tolar Beth, Pettengill James, Batz Michael, Bazaco Michael, Tameru Berhanu, Cui Zhaohui, Lindsey Rebecca L, Simmons Mustafa, Chen Jess, Posny Drew, Carleton Heather, Bruce Beau B
Emerg Infect Dis. 2025 Apr;31(4):783-790. doi: 10.3201/eid3104.241172.
Salmonella enterica bacteria are a leading cause of foodborne illness in the United States; however, most Salmonella illnesses are not associated with known outbreaks, and predicting the source of sporadic illnesses remains a challenge. We used a supervised random forest model to determine the most likely sources responsible for human salmonellosis cases in the United States. We trained the model by using whole-genome multilocus sequence typing data from 18,661 Salmonella isolates from collected single food sources and used feature selection to determine the subset of loci most influential for prediction. The overall out-of-bag accuracy of the trained model was 91%; the highest prediction accuracy was for chicken (97%). We applied the trained model to 6,470 isolates from humans with unknown exposure to predict the source of infection. Our model predicted that >33% of the human-derived Salmonella isolates originated from chicken and 27% were from vegetables.
肠炎沙门氏菌是美国食源性疾病的主要病因；然而，大多数沙门氏菌病与已知的疫情爆发无关，预测散发病例的来源仍然是一项挑战。我们使用了一种有监督的随机森林模型来确定导致美国人类沙门氏菌病病例的最可能来源。我们通过使用来自从单一食物来源收集的18661株沙门氏菌分离株的全基因组多位点序列分型数据来训练该模型，并使用特征选择来确定对预测最有影响的基因座子集。训练模型的总体袋外准确率为91%；对鸡肉的预测准确率最高（97%）。我们将训练好的模型应用于6470株来源不明的人类分离株，以预测感染源。我们的模型预测，超过33%的源自人类的沙门氏菌分离株来自鸡肉，27%来自蔬菜。
