发表机构
Shandong University; City University of Hong Kong; Musketeers Foundation Institute of Data Science, The University of Hong Kong; Department of Data and Systems Engineering, The University of Hong Kong; HKU Shanghai Intelligent Computing Research Center; Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong; The University of Hong Kong – Shenzhen Hospital; Shenzhen Loop Area Institute（山东大学; 香港城市大学; 香港大学 Musketeers 基金会数据科学研究所; 香港大学数据与系统工程系; 香港大学上海智能计算研究中心; 香港大学李嘉诚医学院药理学与药学系; 香港大学深圳医院; 深圳河套学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出目标感知网络拆解框架，发现预算稀缺时隔离结构核心个体优于接触者策略，并开发深度强化学习模型RAIL以自适应选择最优干预，最大化人口保护。
AI 中文摘要
在流行病期间，有限的资源决定了关于隔离或监测谁的关键决策。传统的公共卫生策略直觉上优先考虑已知感染者及其直接接触者。在这里，我们揭示了在现实资源约束下，这种以接触者为中心的方法从根本上来说是次优的。通过将这一挑战表述为目标感知的网络拆解，我们发现了反直觉的、依赖于预算的干预转变。生成函数分析表明，当干预预算稀缺时，隔离结构上核心的个体，而非感染者的直接邻居，能保护更大范围的人群。传统的针对邻居的策略只有在可用资源增加时才变得最优。由于这一关键转变点随干预预算、网络拓扑和感染的空间分布动态变化，静态策略必然失败。为了克服这一点，我们开发了风险感知隔离学习（RAIL），这是一个深度强化学习框架，能根据不断演化的剩余网络自适应地选择最优干预措施。在多种合成和真实世界网络中，RAIL持续最大化人口保护并抑制疫情爆发传播。最终，我们的发现挑战了流行的流行病学直觉，为公共卫生及其他复杂系统中针对局部风险的自适应干预建立了一个通用框架。
英文摘要
During epidemics, limited resources dictate critical decisions about whom to isolate or monitor. Conventional public health strategies intuitively prioritize known infected individuals and their immediate contacts. Here, we reveal that this contact-centric approach is fundamentally suboptimal under realistic resource constraints. By formulating this challenge as target-aware network dismantling, we uncover a counter-intuitive, budget-dependent intervention transition. Generating-function analysis demonstrates that when intervention budgets are scarce, isolating structurally central individuals, rather than the immediate neighbours of the infected, protects a vastly larger population. The conventional neighbour-targeting strategy only becomes optimal as available resources increase. Because this critical transition point shifts dynamically with the intervention budget, network topology, and spatial distribution of infections, static strategies inevitably fail. To overcome this, we develop Risk-Aware Isolation Learning (RAIL), a deep reinforcement learning framework that adaptively selects optimal interventions based on the evolving residual network. Across diverse synthetic and real-world networks, RAIL consistently maximizes population protection and suppresses outbreak spread. Ultimately, our findings challenge prevailing epidemiological intuitions, establishing a generalizable framework for adaptive interventions against localized risks in public health and other complex systems.
