Epidemics spread through a diversity of interactions, and affect many healthy people within a short time, resulting in profound social and economic consequences. Although the research community has accumulated overwhelming evidence for the roles of complex networks in the modelling of epidemic dynamics, we are still confronted with the challenge of understanding and controlling spreading processes across different regions.

The theoretical analysis and accurate assessment of the risks of epidemics require the development of novel analytical tools and frameworks. By introducing various techniques and new perspectives, such as statistics and artificial intelligence, more accurate forecasting models and efficient control strategies can be found.

This Special Issue aims to indicate insights and ideas from scholars regarding theoretical analysis and data-driven modelling for policy making and disease intervention. Both research articles and review articles discussing the current state of the art are welcomed.

Potential topics include but are not limited to the following:

• Predicting the potential spread of the disease through complex network modelling
• Estimating the number of potential infections and asymptomatic infections
• Modelling of the spread of infectious diseases in countries and districts
• Big data-driven analysis of relationships between infection networks and social networks
• Uncovering the spatial spread of infectious diseases among cities or countries
• Predicting the risk of regions which are not invaded by the virus by AI-theory
• Identifying risk factors driving spatial transmission
• Comparisons of validity of computational epidemic models
• The effect of mobility of individuals on an epidemic
• Assessing the control measures of an epidemic
• Identifying the spreading/invasion pathways of individual or city epidemic networks
• Identifying the ‘zero’ patient and super spreaders/invaders