Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules
This work addresses disaster resilience in social networks during epidemics, but it is incremental as it builds on existing modeling frameworks.
The study tackled modeling Digital Twin-Oriented Complex Networked Systems to generate networks representing real systems, finding that mitigation policies should target nodes with preferred features for maximum disaster resilience due to higher infection risks.
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.