Predicting the speed of epidemics spreading on networks

Abstract

Global transport and communication networks enable information, ideas and infectious diseases now to spread at speeds far beyond what has historically been possible. To effectively monitor, design, or intervene in such 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. Here, we study these quantities using a message-passing approach to derive simple and effective predictions which are validated against epidemic simulations on a variety of real-world networks with good agreement. In addition to individualized predictions for different nodes, we find an overall sudden transition from low density to almost full network saturation as the contagion develops in time. Our theory is developed and explained in the setting of simple contagions on tree-like networks, but we are also able to show how the method extends remarkably well to complex contagions and highly clustered networks.

References in corpus (7)

• A message passing approach for general epidemic models
• Networks and the Epidemiology of Infectious Disease
• Percolation on sparse networks
• Global disease spread: statistics and estimation of arrival times
• Tight lower bound for percolation threshold on a quasi-regular graph
• Arrival Time Statistics in Global Disease Spread
• Assessing node risk and vulnerability in epidemics on networks

Cited by in corpus (5)

• Understanding spatial propagation using metric geometry with application to the spread of COVID-19 in the United States
• Model-free hidden geometry of complex networks
• Heterogeneity in Outcomes of Repeated Instances of Percolation Experiments
• Coupled effects of epidemic information and risk awareness on contagion
• In Response to COVID-19: Configuration Model of the Epidemic Spreading