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This study proposes an extendable modelling framework for DigitalTwin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generatingnetworks that faithfully represent real systems. Modelling process focuses on(i) features of nodes and (ii) interaction rules for creating connections thatare built based on individual node's preferences. We conduct experiments onsimulation-based DT-CNSs that incorporate various features and rules aboutnetwork growth and different transmissibilities related to an epidemic spreadon these networks. We present a case study on disaster resilience of socialnetworks given an epidemic outbreak by investigating the infection occurrencewithin specific time and social distance. The experimental results show howdifferent levels of the structural and dynamics complexities, concerned withfeature diversity and flexibility of interaction rules respectively, influencenetwork growth and epidemic spread. The analysis revealed that, to achievemaximum disaster resilience, mitigation policies should be targeted at nodeswith preferred features as they have higher infection risks and should be thefocus of the epidemic control.

Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules