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10.3934/mbe.2020153 http://scihub22266oqcxt.onion/10.3934/mbe.2020153 32987496!ä!32987496 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       Math+Biosci+Eng 2020 ; 17 (4): 2792-2804 Nephropedia Template TP {{tp|p=32987496|t=2020. A discrete stochastic model of the COVID-19 outbreak: Forecast and control |pdf=|usr=}}{{32987496}} gab.com Text A discrete stochastic model of the COVID-19 outbreak: Forecast and control JO: Math Biosci Eng http://www.kidney.de/pget.php?&tw=1&pmid=32987496 #FOAvip The novel Coronavirus (COVID-19) is spreading and has caused a large-scale infection in China since December 2019. This has led to a significant impact on the lives and economy in China and other countries. Here we develop a discrete-time stochastic epidemic model with binomial distributions to study the transmission of the disease. Model parameters are estimated on the basis of fitting to newly reported data from January 11 to February 13, 2020 in China. The estimates of the contact rate and the effective reproductive number support the efficiency of the control measures that have been implemented so far. Simulations show the newly confirmed cases will continue to decline and the total confirmed cases will reach the peak around the end of February of 2020 under the current control measures. The impact of the timing of returning to work is also evaluated on the disease transmission given different strength of protection and control measures. Twit Text FOAVip A discrete stochastic model of the COVID-19 outbreak: Forecast and control ????Math Biosci Eng http://www.kidney.de/pget.php?&tw=1&pmid=32987496 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32987496 #FOAvip English Wikipedia <ref>{{Cite pmid|32987496}}</ref> A discrete stochastic model of the COVID-19 outbreak: Forecast and control #MMPMID32987496 He S; Tang SY; Rong L Math Biosci Eng 2020[Mar]; 17 (4): 2792-2804 PMID32987496show ga The novel Coronavirus (COVID-19) is spreading and has caused a large-scale infection in China since December 2019. This has led to a significant impact on the lives and economy in China and other countries. Here we develop a discrete-time stochastic epidemic model with binomial distributions to study the transmission of the disease. Model parameters are estimated on the basis of fitting to newly reported data from January 11 to February 13, 2020 in China. The estimates of the contact rate and the effective reproductive number support the efficiency of the control measures that have been implemented so far. Simulations show the newly confirmed cases will continue to decline and the total confirmed cases will reach the peak around the end of February of 2020 under the current control measures. The impact of the timing of returning to work is also evaluated on the disease transmission given different strength of protection and control measures. |*Betacoronavirus[MESH] |*Models, Biological[MESH] |*Pandemics/prevention & control/statistics & numerical data[MESH] |Basic Reproduction Number/statistics & numerical data[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Computer Simulation[MESH] |Coronavirus Infections/*epidemiology/prevention & control/transmission[MESH] |Humans[MESH] |Mathematical Concepts[MESH] |Pneumonia, Viral/*epidemiology/prevention & control/transmission[MESH] |SARS-CoV-2[MESH]A discrete stochastic model of the COVID-19 outbreak: Forecast and con(epmc).pdf DeepDyve Pubget Overpricing