随机EM算法用于部分观察到的随机流行病,具有个体异质性.
Fan Bu1, Allison E Aiello2, Alexander Volfovsky3
1Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
Biostatistics (Oxford, England)
|August 8, 2024
概括
我们创建了一个新的模型来追踪通过不断变化的社交网络传播的传染病,并考虑到个体差异. 我们的方法准确地估计疾病动态,即使有不完整的数据,改进了流行病分析.
科学领域:
- 流行病学 流行病学
- 网络科学 网络科学
- 计算生物学 计算生物学
背景情况:
- 流行病建模通常简化了联系网络.
- 现实世界的网络是动态的,感染率各不相同.
- 对疾病病例的不完全观察是常见的.
研究的目的:
- 在动态网络上开发流行病的灵活随机模型.
- 为部分观察到的流行病数据创建一个推断方法.
- 准确估计模型参数并了解疾病传播.
主要方法:
- 模拟关节动力学作为连续时间的马尔科夫链.
- 纳入异质感染率和个体共变量.
- 开发了一个随机的预期最大化 (EM) 算法,使用高效的样本来归纳缺失的数据.
主要成果:
- 拟议的EM算法准确地恢复模型参数.
- 该方法有效地处理动态网络和部分观测.
- 在合成和现实世界流行病数据集上展示了性能.
结论:
- 我们的方法为在动态网络上进行流行病建模提供了一个强大的框架.
- 推断方法提供了有价值的见解,尽管没有观察到的疾病发作.
- 这项工作增强了复杂传染病动态的分析.
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