减少流行病网络建模中的规模偏差
Neha Bansal1, Katerina Kaouri1, Thomas E Woolley1
1School of Mathematics, Cardiff University, Senghennydd Road, Cardiff, CF24 4AG, UK.
Journal of theoretical biology
|November 17, 2025
概括
与随机步行 (RW) 采样相比,大都会-哈斯廷斯随机步行 (MHRW) 采样减少了疾病传播模型的偏差,特别是在较慢的流行病中. MHRW为决策提供了更准确的网络表示,除了在无规模网络中.
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:
- 流行病学模型为疾病控制政策提供信息.
- 这些模型经常使用采样接触网络.
- 常见的随机步行 (RW) 采样创建了高度联系的个体的大小偏差,过度代表性样本,扭曲了疾病传播估计.
研究的目的:
- 比较大都会-哈斯廷斯随机步行 (MHRW) 和RW采样算法.
- 评估它们在减少网络采样中大小偏差方面的有效性.
- 评估它们对不同网络结构疾病传播模拟准确性的影响.
主要方法:
- 模拟的疾病传播使用一个随机的易感-感染-恢复 (SIR) 框架.
- 在Erdös-Rényi (ER),小世界 (SW),负二项式 (NB) 和无尺度 (SF) 网络上比较RW和MHRW采样算法.
- 分析了现实世界的牛流动和人类接触网络数据.
主要成果:
- RW高估了NB网络中的感染和二次感染,低估了NB网络中的感染时间.
- 在ER,SW和NB网络中,MHRW显著减少了大小偏差.
- 这两种算法都在SF网络上产生了非代表性和可变估计.
- MHRW提供了更接近基础网络的疾病传播估计,以获得真实世界的数据.
结论:
- 对于较慢,低严重性的流行病和异质网络 (NB),MHRW采样比RW更适合.
- RW适用于同质网络 (ER,SW) 中快速传播的流行病.
- 算法选择取决于网络结构和流行病特征,以便可靠的疾病建模和政策制定.
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