Sideward Contact Tracing in an Epidemic Model with Mixing Groups

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.

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Publication Details

Journal
Methodology And Computing In Applied Probability
Published
2025-04-03
DOI
https://doi.org/10.1007/s11009-025-10160-y
Primary Topic
COVID-19 Digital Contact Tracing
Type
article
Field-Weighted Citation Impact
0.00

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article

Sideward Contact Tracing in an Epidemic Model with Mixing Groups

Martina Favero, Dongni Zhang
Methodology And Computing In Applied Probability
COVID-19 Digital Contact Tracing
article

Sideward Contact Tracing in an Epidemic Model with Mixing Groups

Martina Favero, Dongni Zhang
article en

Abstract

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.

Methodology And Computing In Applied ProbabilityVol. 27(2)
Linköping University (SE), Stockholm University (SE)
Linköpings Universitet, Knut och Alice Wallenbergs Stiftelse, Vetenskapsrådet, Stockholms Universitet
Openalex Percentile: Top 92%
COVID-19 Digital Contact Tracing
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