Assessing node risk and vulnerability in epidemics on networks

Abstract

Which nodes are most vulnerable to an epidemic spreading through a network, and which carry the highest risk of causing a major outbreak if they are the source of the infection? Here we show how these questions can be answered to good approximation using the cavity method. Several curious properties of node vulnerability and risk are explored: some nodes are more vulnerable than others to weaker infections, yet less vulnerable to stronger ones; a node is always more likely to be caught in an outbreak than it is to start one, except when the disease has a deterministic lifetime; the rank order of node risk depends on the details of the distribution of infectious periods.

Note that Figure 2 does not appear in the final published version

References in corpus (12)

• Epidemic processes in complex networks
• Spectral redemption: clustering sparse networks
• A message passing approach for general epidemic models
• Inferring the origin of an epidemic with a dynamic message-passing algorithm
• Percolation on sparse networks
• Bayesian inference of epidemics on networks via Belief Propagation
• Cavity Approach to the Spectral Density of Sparse Symmetric Random Matrices
• The unreasonable effectiveness of tree-based theory for networks with clustering
• Cavity approach to the spectral density of non-Hermitian sparse matrices
• Tight lower bound for percolation threshold on a quasi-regular graph
• Maximum-entropy moment-closure for stochastic systems on networks
• Message passing and moment closure for susceptible-infected-recovered epidemics on finite networks

Cited by in corpus (16)

• Statistical physics of inference: Thresholds and algorithms
• Predicting the speed of epidemics spreading on networks
• Stability of a Giant Connected Component in a Complex Network
• Spectral Theory of Sparse Non-Hermitian Random Matrices
• A message-passing approach to epidemic tracing and mitigation with apps
• Contact-based model for epidemic spreading on temporal networks
• Revealing the Micro-Structure of the Giant Component in Random Graph Ensembles
• Heterogeneous micro-structure of percolation in sparse networks
• Impact of presymptomatic transmission on epidemic spreading in contact networks: A dynamic message-passing analysis
• Message-Passing Methods for Complex Contagions
• Rapid decay in the relative efficiency of quarantine to halt epidemics in networks
• Degree dependent transmission rates in epidemic processes
• Heterogeneity in Outcomes of Repeated Instances of Percolation Experiments
• A re-entrant phase transition in the survival of secondary infections on networks
• How network properties and epidemic parameters influence stochastic SIR dynamics on scale-free random networks
• Vaccination with partial transmission and social distancing on contact networks