Suppressing Epidemic Spreading via Contact Blocking in Temporal Networks

Suppressing Epidemic Spreading via Contact Blocking in Temporal Networks

Zhao, Xunyi (Student TU Delft)
Wang, H. (TU Delft Multimedia Computing)

Benito, Rosa M. (editor)
Cherifi, Chantal (editor)
Cherifi, Hocine (editor)
Moro, Esteban (editor)
Rocha, Luis Mateus (editor)
Sales-Pardo, Marta (editor)

2021

In this paper, we aim to effectively suppress the spread of epidemic/information via blocking/removing a given fraction of the contacts in a temporal (time evolving) human contact network. We consider the SI (Susceptible- Infected) spreading process, on a temporal contact network to illustrate our methodology: an infected node infects a susceptible node with a probability β when a contact happens between the two nodes. We address the question: which contacts should be blocked in order to minimize the average prevalence over time. We firstly propose systematically a set of link properties (centrality metrics) based on the aggregated network of a temporal network, that captures the number of contacts between each node pair. Furthermore, we define the probability that a contact c(i, j, t) is removed as a function of the centrality of the corresponding link l(i, j) in the aggregated network as well as the time t of the contact. Each of the centrality metrics proposed can be thus regarded as a contact removal strategy. Empirical results on six temporal contact networks show that the epidemic can be better suppressed if contacts between node pairs that have fewer contacts are more likely to be removed and if contacts happened earlier are likely removed. A strategy tends to perform better when the average number contacts removed per node pair has a lower variance. Strategies that lead to a lower largest eigenvalue of the aggregated network after contact removal do not mitigate the spreading better. This contradicts the finding in static networks, that a network with a small largest eigenvalue tends to be robust against epidemic spreading, illustrating the complexity introduced by the underlying temporal networks.

Epidemic mitigation
SI spreading
Temporal network

http://resolver.tudelft.nl/uuid:bc630552-8061-47d7-b87b-ded59fcf3834

https://doi.org/10.1007/978-3-030-65347-7_37

Springer, Cham

2021-12-20

978-3-030-65346-0

Complex Networks and Their Applications IX: Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020, 1

9th International Conference on Complex Networks and Their Application, COMPLEX NETWORKS 2020, 2020-12-01 → 2020-12-03, Madrid, Spain

Studies in Computational Intelligence, 1860-949X, 943

Accepted author manuscript

Institutional Repository

conference paper

© 2021 Xunyi Zhao, H. Wang