Search

Cascading effects from global disruptions such as natural disasters and pandemics have attracted significant research attention. Current approaches face challenges in adequately integrating geographic and systemic factors, limiting their ability to simulate the intricate dynamics of interdependent systems. Here, we proposed a novel Interdependency Network-based Geographic Cascade (INGC) model, coupling geographic factors to capture cascading shocks across global interdependent networks. By integrating macro-level interdependencies and typical dynamic network modelling approaches, the INGC enables more accurate simulations of hazard damage and shock propagation, highlighting critical nodes and pathways essential for informed policy-making. Through the global lockdown case analysis, the INGC model demonstrated its advantages in identifying critical sectors and regions by revealing heterogenous cascading patterns and their details robustly. This approach offers a scalable framework for future research and policy, ensuring greater resilience in the face of complex global extreme events. • We proposed a geographic cascade model based on interdependency networks. • INGC model can quantify losses, detect propagation, assess risks of cascades. • The model showed different cascading patterns in the global lockdown case. • INGC outputs provide key warnings to mitigate losses and allocate resources. [ABSTRACT FROM AUTHOR]