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.
Bibliographical noteFunding Information:
We would like to thank Zhenlong Li and Huan Ning for their assistance with data preprocessing, as well as Yu Liu and Alan Murray for helpful comments. We also thank the Spatial Innovation Lab and U-Spatial at the University of Minnesota for supporting this research. This work was partially supported by the National Institutes of Health supported Minnesota Population Center (R24 HD041023), the National Spatiotemporal Population Research Infrastructure (2R01HD057929-11) and the New Faculty Set-up Funding from College of Liberal Arts, University of Minnesota. The authors gratefully acknowledge the assistance of the editor and anonymous reviewers. Responsibility for the opinions expressed herein is solely that of the authors.
© 2021, The Author(s).
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, Non-U.S. Gov't