Title: Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning Show Chinese title

Title: 使用机器学习预测弯曲菌属的抗菌药物耐药性（AMR）及其成本负担分析

Abstract: Antimicrobial resistance (AMR) poses a significant public health and economic challenge, increasing treatment costs and reducing antibiotic effectiveness. This study employs machine learning to analyze genomic and epidemiological data from the public databases for molecular typing and microbial genome diversity (PubMLST), incorporating data from UK government-supported AMR surveillance by the Food Standards Agency and Food Standards Scotland. We identify AMR patterns in Campylobacter jejuni and Campylobacter coli isolates collected in the UK from 2001 to 2017. The research integrates whole-genome sequencing (WGS) data, epidemiological metadata, and economic projections to identify key resistance determinants and forecast future resistance trends and healthcare costs. We investigate gyrA mutations for fluoroquinolone resistance and the tet(O) gene for tetracycline resistance, training a Random Forest model validated with bootstrap resampling (1,000 samples, 95% confidence intervals), achieving 74% accuracy in predicting AMR phenotypes. Time-series forecasting models (SARIMA, SIR, and Prophet) predict a rise in campylobacteriosis cases, potentially exceeding 130 cases per 100,000 people by 2050, with an economic burden projected to surpass 1.9 billion GBP annually if left unchecked. An enhanced Random Forest system, analyzing 6,683 isolates, refines predictions by incorporating temporal patterns, uncertainty estimation, and resistance trend modeling, indicating sustained high beta-lactam resistance, increasing fluoroquinolone resistance, and fluctuating tetracycline resistance. Show Chinese abstract

Abstract: 抗菌素耐药性（AMR）对公共健康和经济构成了重大挑战，增加了治疗成本并降低了抗生素的有效性。 本研究采用机器学习分析来自公共数据库的基因组和流行病学数据，用于分子分型和微生物基因组多样性（PubMLST），结合了由英国政府支持的食品标准局和苏格兰食品标准局进行的AMR监测数据。 我们识别了在2001年至2017年间在英国收集的空肠弯曲菌和结肠弯曲菌分离株中的AMR模式。 该研究整合了全基因组测序（WGS）数据、流行病学元数据和经济预测，以确定关键的耐药决定因素，并预测未来的耐药趋势和医疗成本。 我们研究了 gyrA 突变与氟喹诺酮类耐药性以及 tet(O) 基因与四环素类耐药性，训练了一个随机森林模型，并通过自助抽样验证（1,000个样本，95%置信区间），在预测AMR表型方面达到了74%的准确率。 时间序列预测模型（SARIMA、SIR和Prophet）预测空肠弯曲菌感染病例将增加，到2050年可能超过每10万人130例，如果放任不管，经济负担预计每年将超过19亿英镑。 一个增强的随机森林系统，分析了6,683个分离株，通过结合时间模式、不确定性估计和耐药趋势建模来优化预测，表明β-内酰胺类耐药性持续较高，氟喹诺酮类耐药性增加，而四环素类耐药性波动。