This study proposes an extendable modelling framework for Digital
Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating
networks that faithfully represent real systems. Modelling process focuses on
(i) features of nodes and (ii) interaction rules for creating connections that
are built based on individual node's preferences. We conduct experiments on
simulation-based DT-CNSs that incorporate various features and rules about
network growth and different transmissibilities related to an epidemic spread
on these networks. We present a case study on disaster resilience of social
networks given an epidemic outbreak by investigating the infection occurrence
within specific time and social distance. The experimental results show how
different levels of the structural and dynamics complexities, concerned with
feature diversity and flexibility of interaction rules respectively, influence
network growth and epidemic spread. The analysis revealed that, to achieve
maximum disaster resilience, mitigation policies should be targeted at nodes
with preferred features as they have higher infection risks and should be the
focus of the epidemic control