Spreading dynamics is a central topic in the physics of complex systems and network science, providing a unified framework for understanding how information, behaviors, and diseases propagate through interactions among system units. In many propagation contexts, spreading processes are influenced by multiple interacting factors, such as information expression patterns, cultural contexts, living environments, cognitive preferences, and public policies, which are difficult to incorporate directly into classical modeling frameworks. Recently, large language models (LLMs) have exhibited strong capabilities in natural language understanding, reasoning, and generation, enabling explicit perception of semantic content and contextual cues in spreading processes, thereby supporting the analysis of the different influencing factors. Beyond serving as external analytical tools, LLMs can also act as interactive agents embedded in propagation systems, potentially influencing spreading pathways and feedback structures. Consequently, the roles and impacts of LLMs on spreading dynamics have become an active and rapidly growing research area across multiple research disciplines. This review provides a comprehensive overview of recent advances in applying LLMs to the study of spreading dynamics across two representative domains: digital epidemics, such as misinformation and rumors, and biological epidemics, including infectious disease outbreaks. We first examine the foundations of epidemic modeling from a complex-systems perspective and discuss how LLM-based approaches relate to traditional frameworks. We then systematically review recent studies from three key perspectives, which are epidemic modeling, epidemic detection and surveillance, and epidemic prediction and management, to clarify how LLMs enhance these areas. Finally, open challenges and potential research directions are discussed.

翻译：传播动力学是复杂系统物理与网络科学的核心议题，为理解信息、行为与疾病如何通过系统单元间的相互作用传播提供了统一框架。在许多传播情境中，传播过程受到多种交互因素的影响，例如信息表达模式、文化背景、生活环境、认知偏好与公共政策，这些因素难以直接纳入经典建模框架。近年来，大型语言模型（LLMs）在自然语言理解、推理与生成方面展现出强大能力，能够显式感知传播过程中的语义内容与情境线索，从而支持对不同影响因素的分析。除了作为外部分析工具，LLMs亦可作为嵌入传播系统的交互式智能体，潜在地影响传播路径与反馈结构。因此，LLMs在传播动力学中的角色与影响已成为跨越多学科领域活跃且快速发展的研究方向。本文综述了LLMs在传播动力学研究中的应用进展，涵盖两个代表性领域：数字流行病（如错误信息与谣言）与生物流行病（包括传染病暴发）。我们首先从复杂系统视角审视流行病建模的基础理论，并讨论基于LLM的方法与传统框架的关联。随后，我们从三个关键视角系统回顾近期研究——流行病建模、流行病检测与监测、流行病预测与管理——以阐明LLMs如何增强这些领域的研究能力。最后，本文探讨了当前面临的开放挑战与潜在研究方向。