Epidemic Spreading and Digital Contact Tracing: Effects of Heterogeneous Mixing and Quarantine Failures

Abstract

Contact tracing via digital tracking applications installed on mobile phones is an important tool for controlling epidemic spreading. Its effectivity can be quantified by modifying the standard methodology for analyzing percolation and connectivity of contact networks. We apply this framework to networks with varying degree distributions, numbers of application users, and probabilities of quarantine failures. Further, we study structured populations with homophily and heterophily and the possibility of degree-targeted application distribution. Our results are based on a combination of explicit simulations and mean-field analysis. They indicate that there can be major differences in the epidemic size and epidemic probabilities which are equivalent in the normal SIR processes. Further, degree heterogeneity is seen to be especially important for the epidemic threshold but not as much for the epidemic size. The probability that tracing leads to quarantines is not as important as the application adoption rate. Finally, both strong homophily and especially heterophily with regard to application adoption can be detrimental. Overall, epidemic dynamics are very sensitive to all of the parameter values we tested out, which makes the problem of estimating the effect of digital contact tracing an inherently multidimensional problem.

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Cited by in corpus (12)

• Herd Immunity and Epidemic Size in Networks with Vaccination Homophily
• Epidemic control in networks with cliques
• Directed Percolation in Temporal Networks
• Directed Percolation in Random Temporal Network Models with Heterogeneities
• Predicting the diversity of early epidemic spread on networks
• Reticula: A temporal network and hypergraph analysis software package
• Spreading dynamics in networks under context-dependent behavior
• Effectiveness of Contact Tracing on Networks with Cliques
• Homophily Within and Across Groups
• Strength and weakness of disease-induced herd immunity in networks
• Limited efficacy of forward contact tracing in epidemics
• Epidemic models with digital and manual contact tracing