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10.1017/S0950268821000339 http://scihub22266oqcxt.onion/10.1017/S0950268821000339 33563354!7900669!33563354     free     free     free 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 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 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Epidemiol+Infect 2021 ; 149 (ä): e43 Nephropedia Template TP {{tp|p=33563354|t=2021. A simple transmission dynamics model for predicting the evolution of COVID-19 under control measures in China |pdf=|usr=}}{{33563354}} gab.com Text A simple transmission dynamics model for predicting the evolution of COVID-19 under control measures in China JO: Epidemiol Infect http://www.kidney.de/pget.php?&tw=1&pmid=33563354 #FOAvip Epidemic forecasting provides an opportunity to predict geographic disease spread and counts when an outbreak occurs and plays a key role in preventing or controlling their adverse impact. However, conventional prediction models based on complex mathematical modelling rely on the estimation of model parameters, which yields unreliable and unsustainable results. Herein, we proposed a simple model for predicting the epidemic transmission dynamics based on nonlinear regression of the epidemic growth rate and iterative methods, which is applicable to the progression of the COVID-19 outbreak under the strict control measures of the Chinese government. Our model yields reliable and accurate results as confirmed by the available data: we predicted that the total number of infections in mainland China would be 91 253, and the maximum number of beds required for hospitalised patients would be 62 794. We inferred that the inflection point (when the growth rate turns from positive to negative) of the epidemic across China would be mid-February, and the end of the epidemic would be in late March. This model is expected to contribute to resource allocation and planning in the health sector while providing a theoretical basis for governments to respond to future global health crises or epidemics. Twit Text FOAVip A simple transmission dynamics model for predicting the evolution of COVID-19 under control measures in China ????Epidemiol Infect http://www.kidney.de/pget.php?&tw=1&pmid=33563354 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33563354 #FOAvip English Wikipedia <ref>{{Cite pmid|33563354}}</ref> A simple transmission dynamics model for predicting the evolution of COVID-19 under control measures in China #MMPMID33563354 Shang C; Yang Y; Chen GY; Shang XD Epidemiol Infect 2021[Feb]; 149 (ä): e43 PMID33563354show ga Epidemic forecasting provides an opportunity to predict geographic disease spread and counts when an outbreak occurs and plays a key role in preventing or controlling their adverse impact. However, conventional prediction models based on complex mathematical modelling rely on the estimation of model parameters, which yields unreliable and unsustainable results. Herein, we proposed a simple model for predicting the epidemic transmission dynamics based on nonlinear regression of the epidemic growth rate and iterative methods, which is applicable to the progression of the COVID-19 outbreak under the strict control measures of the Chinese government. Our model yields reliable and accurate results as confirmed by the available data: we predicted that the total number of infections in mainland China would be 91 253, and the maximum number of beds required for hospitalised patients would be 62 794. We inferred that the inflection point (when the growth rate turns from positive to negative) of the epidemic across China would be mid-February, and the end of the epidemic would be in late March. This model is expected to contribute to resource allocation and planning in the health sector while providing a theoretical basis for governments to respond to future global health crises or epidemics. |*Disease Transmission, Infectious[MESH] |*Epidemics[MESH] |COVID-19/*epidemiology/*transmission[MESH] |China/epidemiology[MESH] |Communicable Disease Control/statistics & numerical data[MESH] |Forecasting[MESH] |Humans[MESH]A simple transmission dynamics model for predicting the evolution of C(epmc).pdf DeepDyve Pubget Overpricing