Department of Geography, Environment and Society, University of Minnesota, Twin Cities, USA.
Beijing Key Lab of Spatial Information Integration and Its Applications, Peking University, Beijing, China.
Sci Rep. 2021 Apr 19;11(1):8396. doi: 10.1038/s41598-021-87902-8.
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.
我们描述了使用网络建模来捕捉 COVID-19 大流行不断变化的时空性质。跟踪 COVID-19 病例随时间和空间的变化的最常见方法是检查一系列提供大流行快照的地图。一系列快照可以传达病例的空间性质，但通常依赖于主观解释来评估大流行在时间和空间上的严重程度如何变化。我们将网络优化的新应用于标准快照系列，以更好地揭示在混合干预措施下，美国大陆 COVID-19 的空间中心如何随时间在空间上转移。我们发现了一个具有稳定大流行中心以及局部和远程相互作用的全球空间转移模式。引入了从空间转移的每日性质派生的指标，以帮助评估区域尺度上的大流行情况。我们还强调了通过局部空间转移来审查大流行病以揭示地区之间和内部的动态关系（如州之间的溢出和集中）的价值。这种基于网络的空间转移来检查 COVID-19 大流行的新方法提供了理解大流行病在地理上传播的新线索。
