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 transmission into the assessment of network dynamics. This approximation of disease spread dynamics is achieved by altering the Laplacian matrix, which in turn highlights why nodes with a high degree are such influential disease spreaders. From this approach, a trichotomy emerges on the definition of an effective spreader where, for susceptible-infected simulations, eigenvector-based selections can either optimise the initial rate of infection, the average rate of infection, or produce the fastest time to full infection of the network. Simulated and real-world human contact networks are examined, with insights also drawn on the effective adaptation of ant colony contact networks to reduce pathogen spread and protect the queen ant.

Dynamical Influence Driven Space System Design

“…Data transfer is a spreading process that can be represented by a network in order to detect the relative influence of nodes [3]. A network of averaged contacts over time, enables the network's adjacency matrix to provide insights into the major pathways for spread, as in [2] for the identification of influential disease spreaders. For space system flow networks, where targets are sources of data and ground stations are sinks, the eigenvectors of the adjacency matrix can detail the relative influence of ground stations in terms of receiving target data.…”

Identifying effective sink node combinations in spacecraft data transfer networks

“…Data transfer is a spreading process that Clark et al (2019) showed can be represented by a network in order to detect the relative influence of nodes. A network of averaged contacts over time enables the network's adjacency matrix to provide insights into the major pathways for spread, where Clark and Macdonald (2021) demonstrated this by identifying influential disease spreaders in contact networks.…”