Abstract

Quarantining and contact tracing are popular ad hoc practices for mitigating epidemic outbreaks. However, few mathematical theories are currently available to asses the role of a network in the effectiveness of these practices. In this paper, we study how the final size of an epidemic is influenced by the procedure that combines contact tracing and quarantining on a network null model: the configuration model. Namely, we suppose that infected vertices may self-quarantine and trace their infector with a given success probability. A traced infector is, in turn, less likely to infect others. We show that the effectiveness of such tracing process strongly depends on the network structure. In contrast to previous findings, the tracing procedure is not necessarily more effective on networks with heterogeneous degrees. We also show that network clustering influences the effectiveness of the tracing process in a non-trivial way: depending on the infectiousness parameter, contact tracing on clustered networks may either be more, or less efficient than on networks without clustering.

Highlights

  • Contact tracing is a frequently used method to control epidemic outbreaks

  • Such compartmental models simplify the structure of contact networks by representing it with one numerical parameter

  • The contact network of the HIV/AIDS epidemic in Cuba was found to be well-approximated by a power-law degree distribution [6], so that the proportion of vertices with k neighbors scale as k−τ

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Summary

Introduction

Contact tracing is a frequently used method to control epidemic outbreaks. In this method, individuals who show symptoms of a disease, report themselves and identify their recent contacts which are tested for the disease. We investigate the effect of degree-heterogeneity and clustering on the effect of contact tracing on the final outbreak size using percolation models and find that clustering can either increase or decrease the effectiveness of tracing processes, depending on the infectiousness of the epidemic. This shows that the interplay between the underlying network structure and the exact choice of tracing process is delicate, and important to take into account.

Network and tracing model
Immediate or delayed tracing: the impact of δ
Immediate tracing
Tracing with delay
Final outbreak size under contact tracing
The effect of clustering on tracing
Conclusion
A Derivation of the critical value for immediate tracing
B Critical value under delayed tracing
C The giant outbreak size
D Derivation of the giant outbreak size in clustered networks
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