Social Bubbles and Superspreaders: Source Identification for Contagion Processes on Hypertrees

TL;DR

This paper extends source identification methods for contagion processes from simple star networks to complex hypertrees, enabling detection of infection origins in group-based and overlapping social structures.

Contribution

It generalizes maximum likelihood source estimation to hypertrees, accommodating group gatherings and overlapping social bubbles for more accurate outbreak source detection.

Findings

Effective source estimation in hypertrees with overlapping social groups

Applicable to outbreak tracing in complex social networks

Improves accuracy over previous star-based models

Abstract

Previous work has shown that for contagion processes on extended star networks (trees with exactly one node of degree > 2), there is a simple, closed-form expression for a highly accurate approximation to the maximum likelihood infection source. Here, we generalize that result to a class of hypertrees which, although somewhat structurally analogous, provides a much richer representation space. In particular, this approach can be used to estimate patient zero sources, even when the infection has been propagated via large group gatherings rather than person-to-person spread, and when it is spreading through interrelated social bubbles with varying degrees of overlap. In contact tracing contexts, this estimator may be used to identify the source of a local outbreak, which can then be used for forward tracing or for further backward tracing (by similar or other means) to an upstream source.