Network centrality beats neighbor isolation when resources are tight
Target-Aware Network Dismantling with Limited Budgets
Social and Information Networks
Summary
Deciding who to isolate or watch during disease outbreaks is hard when resources are limited. The authors show that instead of focusing on immediate contacts of infected people, isolating key individuals in the network works better when there isn’t much room to act. They created a smart system called RAIL that learns and adapts which people to isolate based on the spreading pattern. Their approach helps protect more people and slows down outbreaks more effectively than traditional methods. This challenges common ideas about disease control and suggests new ways to respond to infectious risks.
What this means in practice
- •For public health planners: Provide adaptive strategies to prioritize isolation of key individuals rather than just contacts, improving outbreak control under limited resources.
- •For infrastructure security teams: Guide adaptive interventions to isolate critical nodes in networks to mitigate risks from localized failures or attacks.
Authors
Jiaqi Song, Fei Jing, Wenjie Huang, Xingqin Qi, Qingpeng Zhang
Abstract
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.