张量网络动态消息传递用于流行病模型
Tensor network dynamical message passing for epidemic models
AI总结：
本文提出张量网络动态消息传递框架，通过易感诱导因子化理论，结合精确算法和近似算法，有效解决流行病模型预测中的效率与准确性权衡问题。
摘要：
尽管流行病学建模在制定公共卫生策略方面至关重要，但一个根本性的权衡限制了其预测的准确性：精确的随机模拟通常对大规模系统来说计算上不可行，而高效的解析近似通常无法考虑必要的短程相关性和网络环。在这里，我们通过引入张量网络动态消息传递（TNDMP）框架来解决这一权衡，该框架基于我们称为
英文摘要：
While epidemiological modeling is pivotal for informing public health strategies, a fundamental trade-off limits its predictive fidelity: exact stochastic simulations are often computationally intractable for large-scale systems, whereas efficient analytical approximations typically fail to account for essential short-range correlations and network loops. Here, we resolve this trade-off by introducing Tensor Network Dynamical Message Passing (TNDMP), a framework grounded in a rigorous property we term \textit{Susceptible-Induced Factorization}. This theoretical insight reveals that a susceptible node acts as a dynamical decoupler, factorizing the global evolution operator into localized components. Leveraging this, TNDMP provides a dual-mode algorithmic suite: an exact algorithm that computes local observables with minimal redundancy on tractable topologies and a scalable and tunable approximation for complex real-world networks. We demonstrate that widely adopted heuristics, such as Dynamical Message Passing (DMP) and Pair Approximation (PA), are mathematically recoverable as low-order limits of our framework. Numerical validation in synthetic and real-world networks confirms that TNDMP significantly outperforms existing methods to predict epidemic thresholds and steady states, offering a rigorous bridge between the efficiency of message passing and the accuracy of tensor network formalisms.
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