We describe the use of network modeling to capture the shifting spatiotemporal nature of the COVID-19 pandemic . The most common approach to tracking COVID-19 cases over time and space is to examine a series of maps that provide snapshots of the pandemic . A series of snapshots can convey the spatial nature of cases but often rely on subjective interpretation to assess how the pandemic is shifting in severity through time and space . We present a novel application of network optimization to a standard series of snapshots to better reveal how the spatial centres of the pandemic shifted spatially over time in the mainland United States under a mix of interventions . We find a global spatial shifting pattern with stable pandemic centres and both local and long-range interactions . Metrics derived from the daily nature of spatial shifts are introduced to help evaluate the pandemic situation at regional scales . We also highlight the value of reviewing pandemics through local spatial shifts to uncover dynamic relationships among and within regions, such as spillover and concentration among states . This new way of examining the COVID-19 pandemic in terms of network-based spatial shifts offers new story lines in understanding how the pandemic spread in geography.