Sideward contact tracing and the control of epidemics in large gatherings

TL;DR

This paper introduces and analyzes a third 'sideward' contact tracing mechanism in large gatherings, demonstrating its impact on epidemic control and proposing optimal gathering sizes for tracing to enhance containment strategies.

Contribution

It identifies and models the role of sideward tracing in epidemic spread within large gatherings, expanding traditional contact tracing methods with a new indirect approach.

Findings

Sideward tracing detects asymptomatic individuals not directly infected or transmitting the disease.

The effectiveness of tracing varies non-monotonically with gathering size.

Optimal gathering sizes for tracing can be determined to improve epidemic suppression.

Abstract

Effective contact tracing is crucial to contain epidemic spreading without disrupting societal activities especially in the present time of coexistence with a pandemic outbreak. Large gatherings play a key role, potentially favouring superspreading events. However, the effects of tracing in large groups have not been fully assessed so far. We show that beside forward tracing, which reconstructs to whom disease spreads, and backward tracing, which searches from whom disease spreads, a third "sideward" tracing is always present, when tracing gatherings. This is an indirect tracing that detects infected asymptomatic individuals, even if they have neither been directly infected by, nor they have directly transmitted the infection to the index case. We analyse this effect in a model of epidemic spreading for SARS-CoV-2, within the framework of simplicial activity-driven temporal networks. We determine the contribution of the three tracing mechanisms to the suppression of epidemic spreading, showing that sideward tracing induces a non-monotonic behaviour in the tracing efficiency, as a function of the size of the gatherings. Based on our results, we suggest an optimal choice for the sizes of the gatherings to be traced and we test the strategy on an empirical dataset of gatherings in a University Campus.