Network percolation reveals adaptive bridges of the mobility network response to COVID-19

TL;DR

This study analyzes the U.S. mobility network during COVID-19 using large-scale trajectory data, revealing critical bridges that influence network connectivity and can inform strategies to control disease spread during disruptions.

Contribution

We developed a novel method to identify key adaptive bridges in the mobility network that are crucial for understanding and managing connectivity during the pandemic.

Findings

Critical bridges connect major network components and influence disease transmission.

Network characteristics determine the thresholds for phase transitions.

Identified adaptive links act as valves controlling regional connectivity.

Abstract

Human mobility is crucial to understand the transmission pattern of COVID-19 on spatially embedded geographic networks. This pattern seems unpredictable, and the propagation appears unstoppable, resulting in over 350,000 death tolls in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county mobility network using 10 TB (Terabytes) trajectory data of 30 million smart devices in the U.S. in the first six months of 2020. We investigate its bound percolation by removing the weakly connected edges. The mobility network becomes vulnerable and prone to reach its criticality and thus experience surprisingly abrupt phase transitions. Despite the complex behaviors of the mobility network, we devised a novel approach to identify a small, manageable set of recurrent critical bridges, connecting the giant component and the second-largest component. These adaptive links, located across the United States, played a key role as valves connecting components in divisions and regions during the pandemic. Beyond, our numerical results unveil that network characteristics determine the critical thresholds and the bridge locations. The findings provide new insights into managing and controlling the connectivity of mobility networks during unprecedented disruptions. The work can also potentially offer practical future infectious diseases both globally and locally.