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Sideward Contact Tracing in an Epidemic Model with Mixing Groups

Author

Listed:
  • Dongni Zhang

    (Linköping University)

  • Martina Favero

    (Stockholm University)

Abstract

We consider a stochastic epidemic model with sideward contact tracing. We assume that infection is driven by interactions within mixing events (gatherings of two or more individuals). Once an infective is diagnosed, each individual who was infected at the same event as the diagnosed individual is contact traced with some given probability. Assuming few initial infectives in a large population, the early phase of the epidemic is approximated by a branching process with sibling dependencies. To address the challenges given by the dependencies, we consider sibling groups (individuals who become infected at the same event) as macro-individuals and define a macro-branching process. This allows us to derive an expression for the effective macro-reproduction number which corresponds to the effective individual reproduction number and represents a threshold for the behaviour of the epidemic. Through numerical examples, we show how the reproduction number varies with the distribution of the mixing event size, the mean size, the rate of diagnosis and the tracing probability.

Suggested Citation

  • Dongni Zhang & Martina Favero, 2025. "Sideward Contact Tracing in an Epidemic Model with Mixing Groups," Methodology and Computing in Applied Probability, Springer, vol. 27(2), pages 1-20, June.
    • Handle: RePEc:spr:metcap:v:27:y:2025:i:2:d:10.1007_s11009-025-10160-y
    DOI: 10.1007/s11009-025-10160-y
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    References listed on IDEAS

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    1. Dyani Lewis, 2021. "Superspreading drives the COVID pandemic — and could help to tame it," Nature, Nature, vol. 590(7847), pages 544-546, February.
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