Simplicial temporal networks from Wi-Fi data in a University Campus: the effects of restrictions on epidemic spreading
arXiv:2210.15002 · doi:10.3389/fphy.2022.1010929
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.
References in corpus (11)
- The physics of higher-order interactions in complex systems
- Dynamics of person-to-person interactions from distributed RFID sensor networks
- What's in a crowd? Analysis of face-to-face behavioral networks
- Activity driven modeling of time varying networks
- A Survey of COVID-19 Contact Tracing Apps
- Dynamical and bursty interactions in social networks
- Social network dynamics of face-to-face interactions
- An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices
- Burstiness in activity-driven networks and the epidemic threshold
- Quest: Practical and Oblivious Mitigation Strategies for COVID-19 using WiFi Datasets
- WiFi-based Crowd Monitoring and Workspace Planning for COVID-19 Recovery