传染病网络中非平衡阶段过渡的动态和热力学起源
Linqi Wang1,2, Kun Zhang2, Li Xu2
1Center of Theoretical Physics, College of Physics, Jilin University, Changchun 130012, China.
The Journal of chemical physics
|August 28, 2025
概括
这项研究使用统计物理学来模拟流行病的动态,揭示了个体行为和人口密度如何推动阶段过渡. 关键减速和其他指标可以预测适应性网络的突发爆发.
科学领域:
- 流行病动态和网络科学
- 非平衡的统计物理
- 复杂系统分析
背景情况:
- 预测和控制传染病爆发需要了解疫情动态中的阶段过渡.
- 适应性流行病网络, 个体改变行为, 呈现复杂的动态.
- 这些系统的相位过渡往往是突然的,难以预测.
研究的目的:
- 研究适应性流行病网络中不平衡阶段过渡 (分叉) 的物理起源.
- 探索网络属性和行为反应如何影响流行病景观地形和稳定性制度.
- 确定流行病传播中的关键转变的早期预警指标.
主要方法:
- 从非平衡统计物理中应用景观和流量框架.
- 在适应性网络上使用易受感染-恢复-易受感染 (SIRS) 模型.
- 系统检查重新连接率 (行为反应) 和平均节点程度 (接触密度) 的影响.
主要成果:
- 再连接率和节点程度的变化改变了潜在的景观,推动了双稳定和单稳定模式之间的过渡.
- 旋转流被确定为这些转变的不平衡驱动力.
- 生产率量化了热力学成本; 临界减速,时间不可逆转性和闪频率作为早期预警指标.
结论:
- 非平衡的统计物理提供了一个框架来理解适应性流行病网络的阶段过渡.
- 行为适应和人口接触密度是影响流行病制度转变的关键因素.
- 确定早期预警信号可以帮助预测和管理突发的公共卫生危机.
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