WLAN-Log-Based Superspreader Detection in the COVID-19 Pandemic

TL;DR

This paper presents a WLAN-log-based framework for detecting superspreaders during pandemics by constructing contact graphs, identifying candidates via centrality measures, and validating their impact through SEIR simulations, demonstrated on university data.

Contribution

It introduces a novel WLAN-log-based framework combining contact graph analysis and SEIR simulation to identify superspreaders in large-scale real-world settings.

Findings

Superspreaders exist on university campuses and change over time.

Asymmetric contact tracing outperforms symmetric in daily graphs.

SEIR simulation is essential for accurate superspreader identification.

Abstract

Identifying "superspreaders" of disease is a pressing concern for society during pandemics such as COVID-19. Superspreaders represent a group of people who have much more social contacts than others. The widespread deployment of WLAN infrastructure enables non-invasive contact tracing via people's ubiquitous mobile devices. This technology offers promise for detecting superspreaders. In this paper, we propose a general framework for WLAN-log-based superspreader detection. In our framework, we first use WLAN logs to construct contact graphs by jointly considering human symmetric and asymmetric interactions. Next, we adopt three vertex centrality measurements over the contact graphs to generate three groups of superspreader candidates. Finally, we leverage SEIR simulation to determine groups of superspreaders among these candidates, who are the most critical individuals for the spread of disease based on the simulation results. We have implemented our framework and evaluate it over a WLAN dataset with 41 million log entries from a large-scale university. Our evaluation shows superspreaders exist on university campuses. They change over the first few weeks of a semester, but stabilize throughout the rest of the term. The data also demonstrate that both symmetric and asymmetric contact tracing can discover superspreaders, but the latter performs better with daily contact graphs. Further, the evaluation shows no consistent differences among three vertex centrality measures for long-term (i.e., weekly) contact graphs, which necessitates the inclusion of SEIR simulation in our framework. We believe our proposed framework and these results may provide timely guidance for public health administrators regarding effective testing, intervention, and vaccination policies.