Complex networks are widely used to describe many real-world systems. They can be used in social, biological, and technical systems. The network topologies severely affect the phenomena of nonlinear dynamics of epidemiological and digital epidemiological. Extensive real-data analyses revealed that the network topology varies with time and should be described as temporal networks rather than static networks.

To uncover and understand the effects of network temporality on spatio-temporal evolving patterns, critical phenomena, and phase transitions of networked epidemic spreading is a very hot challenge in network science. Developing some data-driven epidemic spreading models that consider the network temporality and dynamical characters simultaneously is critical. Moreover, some advanced theoretical analysis methods should be developed to help investigate the spreading dynamics. Based on the previous two aspects, the control approaches for epidemic spreading on temporal networks are worthy of further study.

This Special Issue aims to provide theoretical and empirical investigations about the epidemic spreading dynamics on temporal networks. We highly encourage original research and review articles from different disciplines, including biology, physics, computer science, mathematics, and sociology.

Potential topics include but are not limited to the following:

• Deterministic/stochastic model of epidemic spreading on temporal networks
• Data-driven model for epidemic spreading on temporal networks
• Source identification on temporal networks
• Influential maximization on temporal networks
• New theoretical approaches for epidemic spreading on temporal networks
• Critical phenomena and phase transitions of epidemic spreading on temporal networks
• Dynamical analysis of epidemic spreading on temporal networks
• Optimal temporal network topology for epidemic spreading
• Controlling epidemic spreading on temporal networks
• Modelling epidemic spreading on temporal networks with distinct mechanisms