Identification of effective spreaders in contact networks using dynamical influence.

ABSTRACT: Contact networks provide insights on disease spread due to the duration of close proximity interactions. For systems governed by consensus dynamics, network structure is key to optimising the spread of information. For disease spread over contact networks, the structure would be expected to be similarly influential. However, metrics that are essentially agnostic to the network's structure, such as weighted degree (strength) centrality and its variants, perform near-optimally in selecting effective spreaders. These degree-based metrics outperform eigenvector centrality, despite disease spread over a network being a random walk process. This paper improves eigenvector-based spreader selection by introducing the non-linear relationship between contact time and the probability of disease trans

SUBMITTER: Clark RA 

PROVIDER: S-EPMC7814176 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature