Identification of effective spreaders in contact networks using dynamical influence
| Autor: | Malcolm Macdonald, Ruaridh Clark |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Computer Networks and Communications Computer science TK Disease spread 01 natural sciences 010305 fluids & plasmas Consensus dynamics 03 medical and health sciences 0103 physical sciences 030304 developmental biology 0303 health sciences Multidisciplinary Research lcsh:T57-57.97 Ant colony Dynamical influence Random walk Network dynamics Computational Mathematics Identification (information) lcsh:Applied mathematics. Quantitative methods Network structure Key (cryptography) Laplacian matrix Centrality |
| Zdroj: | Applied Network Science, Vol 6, Iss 1, Pp 1-18 (2021) Applied Network Science |
| ISSN: | 2364-8228 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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