Evolution of global healthcare trade networks: Structural fracture detection, topological responses, and cross-commodity dependency restructuring

Guihai Yu, Yuwei Kang, Xiaopeng Li, Matjaž Perc and Jernej Završnik

Chaos, Solitons & Fractals, 2026, vol. 202, issue P1

Abstract: We study the evolution of global healthcare trade networks under conditions of systemic stress. Global health crises and geopolitical disruptions have revealed the structural fragility of vaccine and medical device trade systems, yet existing research has lacked a unified framework for detecting structural fractures while capturing the dynamic reconfiguration and decoupling of interdependent networks. To address this gap, we propose an integrated analytical framework that combines graph neural network–based change-point detection, multidimensional topological metrics to trace network evolution, and a dynamic cross-network modeling strategy to examine coupling effects between vaccine and medical device trade. Using longitudinal trade data, we identify multiple critical disruptions — most prominently during the COVID-19 pandemic and the escalation of United States–China tariffs — that triggered asymmetric but significant structural transformations. We show that the vaccine trade network underwent centralized realignment and regional clustering after 2020, while maintaining institutional stability on the supply side. By contrast, the medical device trade network exhibited earlier, more episodic sensitivity to geopolitical shocks but retained a stable core architecture and greater topological resilience. Through cross-network analysis, we demonstrate a clear post-crisis decoupling trend, reflected in the functional divergence of the two systems: vaccine trade has become increasingly centralized and geopolitically managed, whereas medical device trade remains flexible and market-oriented. Our findings highlight the importance of understanding global health trade as a system of structurally distinct yet interconnected subsystems. We argue that our framework provides both methodological and empirical advances for assessing trade network resilience and can inform the design of modular, adaptive, and differentiated governance strategies to buffer future systemic shocks.

Keywords: Graph neural networks; Change-point detection; Topological analysis; Stochastic actor-oriented models (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:202:y:2026:i:p1:s0960077925014870

DOI: 10.1016/j.chaos.2025.117474

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