人工智能用于新兴传染病 (EID) 的精确病毒监测:数据驱动的数字双胞胎元宇宙预见研究
Ting-Yu Lin1, Amy Ming-Fang Yen2, Sam Li-Sheng Chen2
1Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei, Taiwan.
Computers in biology and medicine
|August 7, 2025
概括
这项研究引入了一个数字双胞胎模型,用于使用AI和Metaverse跟踪传染病. 它展示了这项技术如何改善接触者追踪和隔离策略,以获得更好的公共卫生结果.
科学领域:
- 利用人工智能 (AI) 和数字双胞胎技术来控制传染病.
- 将增强现实 (AR) 和混合现实 (MR) 集成到 Metaverse 中,用于公共卫生应用.
背景情况:
- 精确制新兴传染病 (EIDs) 需要先进的策略,如人工智能驱动的病毒分泌模型.
- 数字双胞胎通过AR / MR将物理和虚拟环境合并,为实时监控提供了一种新的解决方案.
- 利用物联网 (IoT) 样式的实验室数据 (病毒泄露) 和患者特征进行EID监控.
研究的目的:
- 开发一个数据驱动的数字双胞胎模型,用于对EID进行精确的病毒监测.
- 创建一个沉浸式的Metaverse框架,用于评估接触者追踪,隔离和隔离协议.
- 将该模型应用于COVID-19疫情,重点关注Alpha和Omicron变种.
主要方法:
- 提出了一个数字双线程架构与时间数据管道.
- 使用RT-PCR测试和人口/临床信息的周期值 (Ct) 数据开发了一个物理双胞胎.
- 采用基于马尔科夫的机器学习来研究传染病动态,在VR中染虚拟队列,并使用AR/MR用于分析和决策双胞胎来评估干预措施.
主要成果:
- 从269个阿尔法变异的物理双胞胎病例中生成了一个虚拟队列,包括100万个模拟病例.
- 决策双胞胎确定了最佳的Ct引导接触追踪窗口,在Ct 18-25的24天追踪中实现了90%的有效性.
- 在经过增强的Omicron病例 (94%在7天后) 和未经增强的病例 (76%) 中,检疫有效性更高.
结论:
- 计算机引导的数字双胞胎模型为EID制提供了一种新的方法,在Metaverse中合并物理和网络领域.
- 强调数字双胞胎框架的可扩展性和适用性,以实现精确的公共卫生.
- 强调了未来医疗保健创新的潜力,强调数据安全和隐私保护.
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