Effectiveness of Contact Tracing on Networks with Cliques
arXiv:2304.10405 · doi:10.1103/PhysRevE.109.024303
Abstract
Contact tracing, the practice of isolating individuals who have been in contact with infected individuals, is an effective and practical way of containing disease spread. Here, we show that this strategy is particularly effective in the presence of social groups: Once the disease enters a group, contact tracing not only cuts direct infection paths but can also pre-emptively quarantine group members such that it will cut indirect spreading routes. We show these results by using a deliberately stylized model that allows us to isolate the effect of contact tracing within the clique structure of the network where the contagion is spreading. This will enable us to derive mean-field approximations and epidemic thresholds to demonstrate the efficiency of contact tracing in social networks with small groups. This analysis shows that contact tracing in networks with groups is more efficient the larger the groups are. We show how these results can be understood by approximating the combination of disease spreading and contact tracing with a complex contagion process where every failed infection attempt will lead to a lower infection probability in the following attempts. Our results illustrate how contact tracing in real-world settings can be more efficient than predicted by models that treat the system as fully mixed or the network structure as locally tree-like.
arXiv comment18 pages, 18 figures, 4 tables
References in corpus (23)
- Epidemic processes in complex networks
- The spread of epidemic disease on networks
- Critical phenomena in complex networks
- Random graphs with clustering
- Properties of highly clustered networks
- Percolation on complex networks: Theory and application
- Second look at the spread of epidemics on networks
- Random graphs containing arbitrary distributions of subgraphs
- Epidemic spreading on complex networks with community structures
- The unreasonable effectiveness of tree-based theory for networks with clustering
- How clustering affects the bond percolation threshold in complex networks
- Universal nonlinear infection kernel from heterogeneous exposure on higher-order networks
- Propagation dynamics on networks featuring complex topologies
- Percolation and the pandemic
- Herd Immunity and Epidemic Size in Networks with Vaccination Homophily
- Sideward contact tracing and the control of epidemics in large gatherings
- A multi-type branching process method for modelling complex contagion on clustered networks
- Epidemic Spreading and Digital Contact Tracing: Effects of Heterogeneous Mixing and Quarantine Failures
- Random graphs with arbitrary clustering and their applications
- Beyond clustering: Mean-field dynamics on networks with arbitrary subgraph composition
- Epidemic control in networks with cliques
- Directed Percolation in Temporal Networks
- Directed Percolation in Random Temporal Network Models with Heterogeneities