A message-passing approach to epidemic tracing and mitigation with apps

Abstract

With the hit of new pandemic threats, scientific frameworks are needed to understand the unfolding of the epidemic. The use of mobile apps that are able to trace contacts is of utmost importance in order to control new infected cases and contain further propagation. Here we present a theoretical approach using both percolation and message--passing techniques, to the role of contact tracing, in mitigating an epidemic wave. We show how the increase of the app adoption level raises the value of the epidemic threshold, which is eventually maximized when high-degree nodes are preferentially targeted. Analytical results are compared with extensive Monte Carlo simulations showing good agreement for both homogeneous and heterogeneous networks. These results are important to quantify the level of adoption needed for contact-tracing apps to be effective in mitigating an epidemic.

arXiv comment(5 pages,4 figures +SM)

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• The theory of percolation on hypergraphs
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• Virus spread versus contact tracing: two competing contagion processes
• Epidemic Spreading and Digital Contact Tracing: Effects of Heterogeneous Mixing and Quarantine Failures
• Impact of presymptomatic transmission on epidemic spreading in contact networks: A dynamic message-passing analysis
• The impact of homophily on digital proximity tracing
• Periodic recurrent waves of Covid-19 epidemics and vaccination campaign
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• Heterogeneous message passing for heterogeneous networks
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• Heterogeneous mean-field theory for two-species symbiotic processes on networks
• Critical time-dependent branching process modelling epidemic spreading with containment measures
• Vaccination with partial transmission and social distancing on contact networks
• Limited efficacy of forward contact tracing in epidemics
• Global hierarchy vs. local structure: spurious self-feedback in scale-free networks
• Epidemics with asymptomatic transmission: Sub-critical phase from recursive contact tracing