Abstract
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.
Highlights
Networks, such as physical contact networks and online social networks, facilitate the spread of epidemics and information
Link centrality metrics We propose a set of link centrality metrics based on node centrality metrics for the aggregated network GW
Besides the proposed strategies based on the aforementioned link centrality metrics, we introduce a baseline strategy called Random removal
Summary
Networks, such as physical contact networks and online social networks, facilitate the spread of epidemics and information. The study of epidemic spreading first assumed the topology of networks to be static (Pastor-Satorras et al 2015; Wang et al 2013), while many real-world networks are not static as nodes and links can appear and disappear over time, can be better represented as temporal networks (Holme and Saramäki 2012). Zhang et al Applied Network Science (2022) 7:2. Epidemic/information spreading can be mitigated via reducing physical contacts. Covid-19 measures like curfew, working at home, social distancing all aim to block physical contacts. These measures treat at least a subgroup of the population in the same way. We propose to develop contact removal strategies utilizing the network properties of contacts
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