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10.3855/jidc.12585 http://scihub22266oqcxt.onion/10.3855/jidc.12585 32235084!ä!32235084 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Infect+Dev+Ctries 2020 ; 14 (3): 246-253 Nephropedia Template TP {{tp|p=32235084|t=2020. Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis |pdf=|usr=}}{{32235084}} gab.com Text Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis JO: J Infect Dev Ctries http://www.kidney.de/pget.php?&tw=1&pmid=32235084 #FOAvip Currently, the outbreak of COVID-19 is rapidly spreading especially in Wuhan city, and threatens 14 million people in central China. In the present study we applied the Moran index, a strong statistical tool, to the spatial panel to show that COVID-19 infection is spatially dependent and mainly spread from Hubei Province in Central China to neighbouring areas. Logistic model was employed according to the trend of available data, which shows the difference between Hubei Province and outside of it. We also calculated the reproduction number R0 for the range of [2.23, 2.51] via SEIR model. The measures to reduce or prevent the virus spread should be implemented, and we expect our data-driven modeling analysis providing some insights to identify and prepare for the future virus control. Twit Text FOAVip Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis ????J Infect Dev Ctries http://www.kidney.de/pget.php?&tw=1&pmid=32235084 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32235084 #FOAvip English Wikipedia <ref>{{Cite pmid|32235084}}</ref> Spatial-temporal distribution of COVID-19 in China and its prediction: A data-driven modeling analysis #MMPMID32235084 Huang R; Liu M; Ding Y J Infect Dev Ctries 2020[Mar]; 14 (3): 246-253 PMID32235084show ga Currently, the outbreak of COVID-19 is rapidly spreading especially in Wuhan city, and threatens 14 million people in central China. In the present study we applied the Moran index, a strong statistical tool, to the spatial panel to show that COVID-19 infection is spatially dependent and mainly spread from Hubei Province in Central China to neighbouring areas. Logistic model was employed according to the trend of available data, which shows the difference between Hubei Province and outside of it. We also calculated the reproduction number R0 for the range of [2.23, 2.51] via SEIR model. The measures to reduce or prevent the virus spread should be implemented, and we expect our data-driven modeling analysis providing some insights to identify and prepare for the future virus control. |*Disease Outbreaks[MESH] |*Logistic Models[MESH] |*Pandemics[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Cities[MESH] |Coronavirus Infections/*epidemiology[MESH] |Forecasting[MESH] |Humans[MESH] |Pneumonia, Viral/*epidemiology[MESH] |SARS-CoV-2[MESH]Spatial-temporal distribution of COVID-19 in China and its prediction:(epmc).pdf DeepDyve Pubget Overpricing