从流行病数据中推断网络属性
Istvan Z Kiss1,2, Luc Berthouze3, Wasiur R KhudaBukhsh4
1Department of Mathematics, University of Sussex, Falmer, Brighton, BN1 9QH, UK. istvan.kiss@nulondon.ac.uk.
Bulletin of mathematical biology
|December 8, 2023
概括
网络流行病模型为疾病传播提供了洞察力. 这项研究表明,动态生存分析 (DSA) 对于从个人级数据推断参数是可靠的,与人口级数据的最大概率估计 (MLE) 不同.
科学领域:
- 流行病学 流行病学
- 网络科学 网络科学
- 统计建模 统计建模
背景情况:
- 传统的流行病模型在高维的网络数据上扎.
- 像对向模型 (PWM) 这样的平均场模型简化了分析,但在统计推断中使用的用途有限.
- 从流行病数据中推断疾病和网络参数是一项挑战.
研究的目的:
- 评估对型模型 (PWM) 与易受感染-康复 (SIR) 动态相结合的有效性,以推断疾病和网络参数.
- 为了比较使用人口层面与个人层面的流行病数据的统计推断方法.
- 评估推理方法的稳定性与现实世界的场景中的模型不匹配.
主要方法:
- 使用对对模型 (PWM) 与易受感染-恢复 (SIR) 流行动态.
- 用人最大概率估计 (MLE) 用于人口级数据 (例如,每日新增病例).
- 应用动态生存分析 (DSA) 对个人级数据 (例如恢复时间).
主要成果:
- 无论是MLE还是DSA都在模拟数据上表现良好 (没有模型不匹配).
- DSA证明了与现实数据 (例如,口,H1N1,COVID-19) 的模型不匹配的稳定性,产生了可信的流行病学参数.
- MLE在与现实数据作斗争,显示了参数不可识别性和对人口规模和报告不足的敏感性问题.
结论:
- 动态生存分析 (DSA) 是一种更强大的方法,可以从网络上的个体级流行病数据中推断参数,特别是使用现实数据.
- 基于网络的平均场模型可以根据近似概率进行调整,从而能够推断疾病动态和网络结构.
- 未来的研究应该集中在基于网络的流行病模型的高效推理方案上.
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