在无症状患者的存在下,通过接触追踪来识别来源
Gergely Ódor1,2, Jana Vuckovic1, Miguel-Angel Sanchez Ndoye1
1EPFL, Lausanne, Switzerland.
概括
在没有完整的联系人数据的情况下,在大型网络中识别零患者是具有挑战性的. 我们新的通过联系人追踪框架 (SICTF) 识别来源使用自适应查询来有效地找到来源,即使是无症状病例.
科学领域:
- 网络科学 网络科学
- 流行病学 流行病学
- 计算生物学 计算生物学
背景情况:
- 确定疾病传播的起源 (零患者) 对于控制流行病至关重要.
- 目前的方法通常需要对接触网络的完整知识,这对于疾病爆发等现实世界的场景来说是不切实际的.
- 无症状的个体对传统的来源识别方法构成重大挑战.
研究的目的:
- 开发一种新的框架,用于识别大型网络中传播的来源,对联系结构的预先了解有限.
- 提出和评估适应性算法,以使用最小的接触和测试查询来有效识别源.
- 为了应对无症状病原体在来源识别方面的挑战.
主要方法:
- 通过联系追踪框架 (SICTF) 引入了源标识,它从最小的信息开始,并适应地查询网络.
- 实施了两个本地搜索算法:LS (数据效率高但对无症状药物敏感) 和LS+ (对无症状药物更强大).
- 使用基于随机指数树的模拟和分析近似来评估算法性能.
主要成果:
- 无论是LS还是LS+算法都超过了现有的源识别方法,即使基线算法拥有完全的网络访问权限.
- 即使在存在众多无症状病原体的情况下,LS+也在识别来源方面表现出强大.
- 来源识别概率的分析近似与模拟结果非常相匹配.
结论:
- 拟议的SICTF提供了一种有效的方法,用于识别大型,部分未知的网络中的疾病来源.
- LS和LS+算法为源识别提供了高效和强大的解决方案,特别是在无症状个体的复杂场景中.
- 该框架的性能在现实的COVID-19模拟数据集上得到了验证,证明了其实际适用性.
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