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引用次数: 0
摘要
找到流行病的源头非常重要,因为正确的源头识别有助于阻止流行病的萌芽或预防新的流行病。我们研究了 N 个交织平均场近似易感-传染-易感(SIS)过程的后向方程。利用后向方程,我们可以在规模至少为 N=1500 的网络上追溯疫情源头。此外,我们还证明,即使在 "最佳情况 "下,即已知完全描述流行病过程和底层接触网络的无穷小发生器 Q,也无法找到 "更现实 "的马尔可夫 SIS 模型的源头。当病毒状态向量 s(t) 在某个时间 t 已知时,可以通过分析找到揭示流行源的马尔可夫初始条件 s(0),即 s(0)=s(t)e^{-Qt}。然而,除了小时间 t 外,s(0) 几乎无法计算。数值误差主要是由于矩阵指数 e^{-Qt} 的严重乖离造成的。
Finding patient zero in susceptible-infectious-susceptible epidemic processes.
Finding the source of an epidemic is important, because correct source identification can help to stop a budding epidemic or prevent new ones. We investigate the backward equations of the N-intertwined mean-field approximation susceptible-infectious-susceptible (SIS) process. The backward equations allow us to trace the epidemic back to its source on networks of sizes up to at least N=1500. Additionally, we show that the source of the "more realistic" Markovian SIS model cannot feasibly be found, even in a "best-case scenario," where the infinitesimal generator Q, which completely describes the epidemic process and the underlying contact network, is known. The Markovian initial condition s(0), which reveals the epidemic source, can be found analytically when the viral state vector s(t) is known at some time t as s(0)=s(t)e^{-Qt}. However, s(0) can hardly be computed, except for small times t. The numerical errors are largely due to the matrix exponential e^{-Qt}, which is severely ill-behaved.
期刊介绍:
Physical Review E (PRE), broad and interdisciplinary in scope, focuses on collective phenomena of many-body systems, with statistical physics and nonlinear dynamics as the central themes of the journal. Physical Review E publishes recent developments in biological and soft matter physics including granular materials, colloids, complex fluids, liquid crystals, and polymers. The journal covers fluid dynamics and plasma physics and includes sections on computational and interdisciplinary physics, for example, complex networks.