一个新的自我适应的SIS模型,基于图形及其线图之间的互动
Paolo Bartesaghi1, Gian Paolo Clemente2, Rosanna Grassi1
1Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milano, Italy.
Chaos (Woodbury, N.Y.)
|February 16, 2024
概括
这项研究引入了一种基于网络的新型流行病模型,其中节点和边缘都可以被感染,实时适应. 这种适应性易感-感染-易感模型增强了扩散动态,并引入了一个新的自向量中心度量.
科学领域:
- 流行病学 流行病学
- 网络科学 网络科学
- 复杂的系统复杂的系统.
背景情况:
- 标准的流行病模型往往忽略了网络结构的复杂动态.
- 网络拓和疾病传播之间的相互作用对于理解扩散过程至关重要.
研究的目的:
- 提出一个新的基于网络的自我适应的流行病模型.
- 调查网络结构及其线图对流行病动态的影响.
- 引入一种基于网络和边缘属性的新型自向量中心性测量方法.
主要方法:
- 在网络及其线图上实施易受感染易受感染 (SIS) 模型.
- 对特有和无疾病状态的存在和稳定条件的分析.
- 开发和应用一个新的自向量中心度指标.
- 在合成图形 (循环,正规,恒星) 上进行数值模拟.
主要成果:
- 拟议的模型展示了基于感染概率的图形权重的实时重新调制.
- 图形与其线图之间的合作用作为扩散的增强因子.
- 引入了一个新的自身向量中心性,考虑邻近的节点和连接的边缘.
- 数字模拟验证了模型捕捉实证行为采用机制的能力.
结论:
- 基于网络的自我适应性流行病模型为研究疾病传播提供了更现实的方法.
- 该模型的适应性和新型的中心性措施为网络动态提供了新的见解.
- 这些发现对理解复杂系统中的扩散和采用过程有影响.
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