用机器学习预测沙门氏菌耐药趋势，预估未来医疗成本超19亿英镑
Predicting Antimicrobial Resistance (AMR) in Campylobacter, a Foodborne Pathogen, and Cost Burden Analysis Using Machine Learning
- 基于全基因组数据和流行病学信息，构建随机森林模型预测耐药性
- 模型准确率达74%，预测2050年每10万人将超130例感染病例
- 可为公共卫生政策提供耐药趋势与经济负担的决策支持
抗菌药物耐药性（AMR）对公共健康和经济构成重大挑战，导致治疗成本上升且抗生素效果降低。本研究利用机器学习分析来自公开数据库PubMLST的基因组与流行病学数据，整合英国食品标准局和苏格兰食品标准局支持的耐药性监测数据，针对2001至2017年间英国采集的弯曲杆菌（Campylobacter jejuni 和 Campylobacter coli）分离株进行分析。研究结合全基因组测序（WGS）数据、流行病学元数据及经济预测，识别关键耐药决定因素，并预测未来耐药趋势与医疗成本。通过随机森林模型（1,000次自助抽样，95%置信区间）验证，对氟喹诺酮类耐药性（基于gyrA突变）和四环素类耐药性（基于tet(O)基因）的预测准确率达74%。时间序列模型（SARIMA、SIR、Prophet）预测，若无干预，2050年弯曲杆菌病发病率可能超过每10万人130例，年度经济负担将突破19亿英镑。增强型随机森林系统分析6,683个菌株，引入时间模式、不确定性估计与耐药趋势建模，揭示β-内酰胺类耐药持续高发，氟喹诺酮类耐药性上升，四环素类耐药性波动。
原文摘要 · Abstract (English)
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.
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