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10.1371/journal.pone.0238280 http://scihub22266oqcxt.onion/10.1371/journal.pone.0238280 32853285!7451659!32853285     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2020 ; 15 (8): e0238280 Nephropedia Template TP {{tp|p=32853285|t=2020. Predicting and analyzing the COVID-19 epidemic in China: Based on SEIRD, LSTM and GWR models |pdf=|usr=}}{{32853285}} gab.com Text Predicting and analyzing the COVID-19 epidemic in China: Based on SEIRD, LSTM and GWR models JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32853285 #FOAvip In December 2019, the novel coronavirus pneumonia (COVID-19) occurred in Wuhan, Hubei Province, China. The epidemic quickly broke out and spread throughout the country. Now it becomes a pandemic that affects the whole world. In this study, three models were used to fit and predict the epidemic situation in China: a modified SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) dynamic model, a neural network method LSTM (Long Short-Term Memory), and a GWR (Geographically Weighted Regression) model reflecting spatial heterogeneity. Overall, all the three models performed well with great accuracy. The dynamic SEIRD prediction APE (absolute percent error) of China had been </= 1.0% since Mid-February. The LSTM model showed comparable accuracy. The GWR model took into account the influence of geographical differences, with R2 = 99.98% in fitting and 97.95% in prediction. Wilcoxon test showed that none of the three models outperformed the other two at the significance level of 0.05. The parametric analysis of the infectious rate and recovery rate demonstrated that China's national policies had effectively slowed down the spread of the epidemic. Furthermore, the models in this study provided a wide range of implications for other countries to predict the short-term and long-term trend of COVID-19, and to evaluate the intensity and effect of their interventions. Twit Text FOAVip Predicting and analyzing the COVID-19 epidemic in China: Based on SEIRD, LSTM and GWR models ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32853285 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32853285 #FOAvip English Wikipedia <ref>{{Cite pmid|32853285}}</ref> Predicting and analyzing the COVID-19 epidemic in China: Based on SEIRD, LSTM and GWR models #MMPMID32853285 Liu F; Wang J; Liu J; Li Y; Liu D; Tong J; Li Z; Yu D; Fan Y; Bi X; Zhang X; Mo S PLoS One 2020[]; 15 (8): e0238280 PMID32853285show ga In December 2019, the novel coronavirus pneumonia (COVID-19) occurred in Wuhan, Hubei Province, China. The epidemic quickly broke out and spread throughout the country. Now it becomes a pandemic that affects the whole world. In this study, three models were used to fit and predict the epidemic situation in China: a modified SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) dynamic model, a neural network method LSTM (Long Short-Term Memory), and a GWR (Geographically Weighted Regression) model reflecting spatial heterogeneity. Overall, all the three models performed well with great accuracy. The dynamic SEIRD prediction APE (absolute percent error) of China had been |*Models, Statistical[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Coronavirus Infections/*epidemiology[MESH] |Geography, Medical[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH]Predicting and analyzing the COVID-19 epidemic in China: Based on SEIR(epmc).pdf DeepDyve Pubget Overpricing