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10.1038/s41598-021-87902-8 http://scihub22266oqcxt.onion/10.1038/s41598-021-87902-8 33875751!8055907!33875751     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2021 ; 11 (1): 8396 Nephropedia Template TP {{tp|p=33875751|t=2021. Revealing the spatial shifting pattern of COVID-19 pandemic in the United States |pdf=|usr=}}{{33875751}} gab.com Text Revealing the spatial shifting pattern of COVID-19 pandemic in the United States JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33875751 #FOAvip 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. Twit Text FOAVip Revealing the spatial shifting pattern of COVID-19 pandemic in the United States ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33875751 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33875751 #FOAvip English Wikipedia <ref>{{Cite pmid|33875751}}</ref> Revealing the spatial shifting pattern of COVID-19 pandemic in the United States #MMPMID33875751 Zhu D; Ye X; Manson S Sci Rep 2021[Apr]; 11 (1): 8396 PMID33875751show ga 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. |COVID-19/epidemiology/*pathology/virology[MESH] |Humans[MESH] |Models, Theoretical[MESH] |Pandemics[MESH] |SARS-CoV-2/isolation & purification[MESH] |Spatio-Temporal Analysis[MESH]Revealing the spatial shifting pattern of COVID-19 pandemic in the Uni(epmc).pdf DeepDyve Pubget Overpricing