American Physical Society February 12, 2020
To effectively monitor, design, or intervene in epidemic-like processes, there is a need to predict the speed of a particular contagion in a particular network, and to distinguish between nodes that are more likely to become infected sooner or later during an outbreak. Researchers in the UK studied global transport and communication networks using a message-passing approach to derive simple and effective predictions that are validated against epidemic simulations on a variety of real-world networks with good agreement. In addition to individualized predictions for different nodes, they found an overall sudden transition from low density to almost full network saturation as the contagion progresses in time. Their theory is developed and explained in the setting of simple contagions on treelike networks, but they are also able to show how the method extends remarkably well to complex contagions and highly clustered networks…read more. TECHNICAL ARTICLE