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10.3934/mbe.2020172 http://scihub22266oqcxt.onion/10.3934/mbe.2020172 32987515!ä!32987515 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Math+Biosci+Eng 2020 ; 17 (4): 3040-3051 Nephropedia Template TP {{tp|p=32987515|t=2020. Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data |pdf=|usr=}}{{32987515}} gab.com Text Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data JO: Math Biosci Eng http://www.kidney.de/pget.php?&tw=1&pmid=32987515 #FOAvip We model the COVID-19 coronavirus epidemic in China. We use early reported case data to predict the cumulative number of reported cases to a final size. The key features of our model are the timing of implementation of major public policies restricting social movement, the identification and isolation of unreported cases, and the impact of asymptomatic infectious cases. Twit Text FOAVip Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data ????Math Biosci Eng http://www.kidney.de/pget.php?&tw=1&pmid=32987515 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32987515 #FOAvip English Wikipedia <ref>{{Cite pmid|32987515}}</ref> Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data #MMPMID32987515 Liu ZH; Magal P; Seydi O; Webb G Math Biosci Eng 2020[Apr]; 17 (4): 3040-3051 PMID32987515show ga We model the COVID-19 coronavirus epidemic in China. We use early reported case data to predict the cumulative number of reported cases to a final size. The key features of our model are the timing of implementation of major public policies restricting social movement, the identification and isolation of unreported cases, and the impact of asymptomatic infectious cases. |*Betacoronavirus[MESH] |*Models, Biological[MESH] |*Pandemics/prevention & control/statistics & numerical data[MESH] |Asymptomatic Infections/epidemiology[MESH] |Basic Reproduction Number/statistics & numerical data[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Computer Simulation[MESH] |Coronavirus Infections/*epidemiology/prevention & control/transmission[MESH] |Epidemics/prevention & control/statistics & numerical data[MESH] |Humans[MESH] |Mathematical Concepts[MESH] |Pneumonia, Viral/*epidemiology/prevention & control/transmission[MESH] |Quarantine/statistics & numerical data[MESH] |SARS-CoV-2[MESH]Predicting the cumulative number of cases for the COVID-19 epidemic in(epmc).pdf DeepDyve Pubget Overpricing