Speaker
Description
We consider a stochastic epidemic model with sideward contact tracing. Infection is assumed to occur through mixing events, that is, gatherings of two or more individuals \cite{ball2022epidemic}. When an infective is diagnosed, each person infected at the same event is traced with a given probability. Instead of tracing who infected a diagnosed person or whom they later infected, sideward tracing identifies people who were infected at the same event. This makes it especially relevant in settings such as large gatherings and potential superspreading events.
Assuming a small number of initial infectives in a large population, the early phase of the epidemic can be approximated by a branching process with sibling dependencies. To address these dependencies, we group together individuals infected at the same event and treat them as macro-individuals, leading to a corresponding macro-branching process. This allows us to derive an effective reproduction number that acts as an epidemic threshold, with critical value 1.
The results show that the sideward tracing becomes more effective when more infections occur within the same event. At the same time, if gatherings are very large, sideward tracing alone may not be sufficient to bring the effective reproduction number below 1.
Bibliography
@article{ball2022epidemic,
title={An epidemic model with short-lived mixing groups},
author={Ball, F. and Neal, P.},
journal={Journal of Mathematical Biology},
volume={85},
number={6},
pages={63},
year={2022},
publisher={Springer}
}
