Simplicial temporal networks from Wi-Fi data in a University Campus: the effects of restrictions on epidemic spreading

TL;DR

This study analyzes Wi-Fi data from a university campus to understand how restrictions impact mobility and epidemic spreading, using a simplicial temporal network model to assess changes in outbreak potential across different lockdown phases.

Contribution

The paper introduces an analysis of Wi-Fi network data to model epidemic spread using simplicial temporal networks, highlighting how restrictions influence contact patterns and outbreak risks.

Findings

Contact networks increase significantly during full opening.

Additional measures are needed to control epidemic spread.

Hotspot ranking correlates with crowding and link formation probability.

Abstract

Wireless networks are commonly used in public spaces, universities and public institutions and provide accurate and easily accessible information to monitor the mobility and behavior of users. Following the application of containment measures during the recent pandemic, we analyse extensive data from the WiFi network in a University Campus in Italy during three periods, corresponding to partial lockdown, partial opening, and almost complete opening. We measure the probability distributions of groups and link activation at Wi-Fi Access Points, investigating how different areas are used in the presence of restrictions. We rank the hotspots and the area they cover according to their crowding and to the probability of link formation, which is the relevant variable in determining potential outbreaks. We consider a recently proposed epidemic model on simplicial temporal networks and we use the measured distributions to infer the change in the reproduction number in the three phases. Our data show that additional measures are necessary to limit the epidemic spreading in the total opening phase, due to the dramatic increase in the number of contacts.