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10.1093/cid/ciaa934 http://scihub22266oqcxt.onion/10.1093/cid/ciaa934 32640020!7454424!32640020     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       Clin+Infect+Dis 2021 ; 72 (4): 643-651 Nephropedia Template TP {{tp|p=32640020|t=2021. Spatiotemporal Characteristics of the COVID-19 Epidemic in the United States |pdf=|usr=}}{{32640020}} gab.com Text Spatiotemporal Characteristics of the COVID-19 Epidemic in the United States JO: Clin Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=32640020 #FOAvip BACKGROUND: A range of near-real-time online/mobile mapping dashboards and applications have been used to track the coronavirus disease 2019 (COVID-19) pandemic worldwide; however, small area-based spatiotemporal patterns of COVID-19 in the United States remain unknown. METHODS: We obtained county-based counts of COVID-19 cases confirmed in the United States from 22 January to 13 May 2020 (N = 1 386 050). We characterized the dynamics of the COVID-19 epidemic through detecting weekly hotspots of newly confirmed cases using Spatial and Space-Time Scan Statistics and quantifying the trends of incidence of COVID-19 by county characteristics using the Joinpoint analysis. RESULTS: Along with the national plateau reached in early April, COVID-19 incidence significantly decreased in the Northeast (estimated weekly percentage change [EWPC]: -16.6%) but continued increasing in the Midwest, South, and West (EWPCs: 13.2%, 5.6%, and 5.7%, respectively). Higher risks of clustering and incidence of COVID-19 were consistently observed in metropolitan versus rural counties, counties closest to core airports, the most populous counties, and counties with the highest proportion of racial/ethnic minorities. However, geographic differences in incidence have shrunk since early April, driven by a significant decrease in the incidence in these counties (EWPC range: -2.0%, -4.2%) and a consistent increase in other areas (EWPC range: 1.5-20.3%). CONCLUSIONS: To substantially decrease the nationwide incidence of COVID-19, strict social-distancing measures should be continuously implemented, especially in geographic areas with increasing risks, including rural areas. Spatiotemporal characteristics and trends of COVID-19 should be considered in decision making on the timeline of re-opening for states and localities. Twit Text FOAVip Spatiotemporal Characteristics of the COVID-19 Epidemic in the United States ????Clin Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=32640020 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32640020 #FOAvip English Wikipedia <ref>{{Cite pmid|32640020}}</ref> Spatiotemporal Characteristics of the COVID-19 Epidemic in the United States #MMPMID32640020 Wang Y; Liu Y; Struthers J; Lian M Clin Infect Dis 2021[Feb]; 72 (4): 643-651 PMID32640020show ga BACKGROUND: A range of near-real-time online/mobile mapping dashboards and applications have been used to track the coronavirus disease 2019 (COVID-19) pandemic worldwide; however, small area-based spatiotemporal patterns of COVID-19 in the United States remain unknown. METHODS: We obtained county-based counts of COVID-19 cases confirmed in the United States from 22 January to 13 May 2020 (N = 1 386 050). We characterized the dynamics of the COVID-19 epidemic through detecting weekly hotspots of newly confirmed cases using Spatial and Space-Time Scan Statistics and quantifying the trends of incidence of COVID-19 by county characteristics using the Joinpoint analysis. RESULTS: Along with the national plateau reached in early April, COVID-19 incidence significantly decreased in the Northeast (estimated weekly percentage change [EWPC]: -16.6%) but continued increasing in the Midwest, South, and West (EWPCs: 13.2%, 5.6%, and 5.7%, respectively). Higher risks of clustering and incidence of COVID-19 were consistently observed in metropolitan versus rural counties, counties closest to core airports, the most populous counties, and counties with the highest proportion of racial/ethnic minorities. However, geographic differences in incidence have shrunk since early April, driven by a significant decrease in the incidence in these counties (EWPC range: -2.0%, -4.2%) and a consistent increase in other areas (EWPC range: 1.5-20.3%). CONCLUSIONS: To substantially decrease the nationwide incidence of COVID-19, strict social-distancing measures should be continuously implemented, especially in geographic areas with increasing risks, including rural areas. Spatiotemporal characteristics and trends of COVID-19 should be considered in decision making on the timeline of re-opening for states and localities. |*COVID-19[MESH] |Humans[MESH] |Incidence[MESH] |Pandemics[MESH] |Rural Population[MESH] |SARS-CoV-2[MESH]Spatiotemporal Characteristics of the COVID-19 Epidemic in the United (epmc).pdf DeepDyve Pubget Overpricing