Contact Tracing & Super-Spreaders in the Branching-Process Model

TL;DR

This paper introduces a novel model for super-spreader events based on random contact rates, analyzing their impact on contact tracing effectiveness using a branching-process approach, with implications for airborne infections like COVID-19.

Contribution

It presents a new super-spreader model not reliant on contact graphs and analyzes how super-spreaders influence contact tracing efficiency.

Findings

Contact tracing is more effective with super-spreaders if symptomatics are few.

High tracing probability enhances effectiveness in the presence of super-spreaders.

Super-spreaders do not significantly hinder contact tracing for SARS-CoV-2.

Abstract

In recent years, it became clear that super-spreader events play an important role, particularly in the spread of airborne infections. We investigate a novel model for super-spreader events, not based on a heterogeneous contact graph but on a random contact rate: Many individuals become infected synchronously in single contact events. We use the branching-process approach for contact tracing to analyze the impact of super-spreader events on the effect of contact tracing. Here we neglect a tracing delay. Roughly speaking, we find that contact tracing is more efficient in the presence of super-spreaders if the fraction of symptomatics is small, the tracing probability is high, or the latency period is distinctively larger than the incubation period. In other cases, the effect of contact tracing can be decreased by super-spreaders. Numerical analysis with parameters suited for SARS-CoV-2 indicates that super-spreaders do not decrease the effect of contact tracing crucially in case of that infection.