  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.
