Mitigate SIR epidemic spreading via contact blocking in temporal networks

• Published in: Applied network science 2022-01, Vol.7 (1), p.2-2, Article 2
• Main Authors: Zhang, Shilun, Zhao, Xunyi, Wang, Huijuan
• Format: Article
• Language: English
• Subjects: Complexity; Computer Appl. in Social and Behavioral Sciences; Computer Science; Contact blocking; Eigenvalues; Epidemic mitigation; Epidemics; Modularity; Networks; Nodes; Science; Simulation and Modeling; Special Issue of the 9th International Conference on Complex Networks and Their Applications; Temporal network
• ISSN: 2364-8228; 2364-8228
• DOI: 10.1007/s41109-021-00436-w

Progress has been made in how to suppress epidemic spreading on temporal networks via blocking all contacts of targeted nodes or node pairs. In this work, we develop contact blocking strategies that remove a fraction of contacts from a temporal (time evolving) human contact network to mitigate the spread of a Susceptible-Infected-Recovered epidemic. We define the probability that a contact c ( i ,  j ,  t ) is removed as a function of a given centrality metric of the corresponding link l ( i ,  j ) in the aggregated network and the time t of the contact. The aggregated network captures the number of contacts between each node pair. A set of 12 link centrality metrics have been proposed and each centrality metric leads to a unique contact removal strategy. These strategies together with a baseline strategy (random removal) are evaluated in empirical contact networks via the average prevalence, the peak prevalence and the time to reach the peak prevalence. We find that the epidemic spreading can be mitigated the best when contacts between node pairs that have fewer contacts and early contacts are more likely to be removed. A strategy tends to perform better when the average number contacts removed from each node pair varies less. The aggregated pruned network resulted from the best contact removal strategy tends to have a large largest eigenvalue, a large modularity and probably a small largest connected component size.