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10.1016/j.chaos.2020.110058 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.110058 C7328553!7328553!32834611     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       Chaos+Solitons+Fractals 2020 ; 139 (ä): 110058 Nephropedia Template TP {{tp|p=32834611|t=2020. Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics |pdf=|usr=}}{{32834611}} gab.com Text Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834611 #FOAvip COVID-19 has now had a huge impact in the world, and more than 8 million people in more than 100 countries are infected. To contain its spread, a number of countries published control measures. However, it?s not known when the epidemic will end in global and various countries. Predicting the trend of COVID-19 is an extremely important challenge. We integrate the most updated COVID-19 epidemiological data before June 16, 2020 into the Logistic model to fit the cap of epidemic trend, and then feed the cap value into FbProphet model, a machine learning based time series prediction model to derive the epidemic curve and predict the trend of the epidemic. Three significant points are summarized from our modeling results for global, Brazil, Russia, India, Peru and Indonesia. Under mathematical estimation, the global outbreak will peak in late October, with an estimated 14.12 million people infected cumulatively. Twit Text FOAVip Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834611 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32834611 #FOAvip English Wikipedia <ref>{{Cite pmid|32834611}}</ref> Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics #MMPMID32834611 Wang P; Zheng X; Li J; Zhu B Chaos Solitons Fractals 2020[Oct]; 139 (ä): 110058 PMID32834611show ga COVID-19 has now had a huge impact in the world, and more than 8 million people in more than 100 countries are infected. To contain its spread, a number of countries published control measures. However, it?s not known when the epidemic will end in global and various countries. Predicting the trend of COVID-19 is an extremely important challenge. We integrate the most updated COVID-19 epidemiological data before June 16, 2020 into the Logistic model to fit the cap of epidemic trend, and then feed the cap value into FbProphet model, a machine learning based time series prediction model to derive the epidemic curve and predict the trend of the epidemic. Three significant points are summarized from our modeling results for global, Brazil, Russia, India, Peru and Indonesia. Under mathematical estimation, the global outbreak will peak in late October, with an estimated 14.12 million people infected cumulatively. äPrediction of epidemic trends in COVID-19 with logistic model and mach(epmc).pdf DeepDyve Pubget Overpricing