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10.1016/j.chaos.2020.110140 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.110140 32834585!7369612!32834585     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Chaos+Solitons+Fractals 2020 ; 138 (ä): 110140 Nephropedia Template TP {{tp|p=32834585|t=2020. Going by the numbers : Learning and modeling COVID-19 disease dynamics |pdf=|usr=}}{{32834585}} gab.com Text Going by the numbers : Learning and modeling COVID-19 disease dynamics JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834585 #FOAvip The COrona VIrus Disease (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has resulted in a challenging number of infections and deaths worldwide. In order to combat the pandemic, several countries worldwide enforced mitigation measures in the forms of lockdowns, social distancing, and disinfection measures. In an effort to understand the dynamics of this disease, we propose a Long Short-Term Memory (LSTM) based model. We train our model on more than four months of cumulative COVID-19 cases and deaths. Our model can be adjusted based on the parameters in order to provide predictions as needed. We provide results at both the country and county levels. We also perform a quantitative comparison of mitigation measures in various counties in the United States based on the rate of difference of a short and long window parameter of the proposed LSTM model. The analyses provided by our model can provide valuable insights based on the trends in the rate of infections and deaths. This can also be of help for countries and counties deciding on mitigation and reopening strategies. We believe that the results obtained from the proposed method will contribute to societal benefits for a current global concern. Twit Text FOAVip Going by the numbers : Learning and modeling COVID-19 disease dynamics ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834585 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32834585 #FOAvip English Wikipedia <ref>{{Cite pmid|32834585}}</ref> Going by the numbers : Learning and modeling COVID-19 disease dynamics #MMPMID32834585 Basu S; Campbell RH Chaos Solitons Fractals 2020[Sep]; 138 (ä): 110140 PMID32834585show ga The COrona VIrus Disease (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has resulted in a challenging number of infections and deaths worldwide. In order to combat the pandemic, several countries worldwide enforced mitigation measures in the forms of lockdowns, social distancing, and disinfection measures. In an effort to understand the dynamics of this disease, we propose a Long Short-Term Memory (LSTM) based model. We train our model on more than four months of cumulative COVID-19 cases and deaths. Our model can be adjusted based on the parameters in order to provide predictions as needed. We provide results at both the country and county levels. We also perform a quantitative comparison of mitigation measures in various counties in the United States based on the rate of difference of a short and long window parameter of the proposed LSTM model. The analyses provided by our model can provide valuable insights based on the trends in the rate of infections and deaths. This can also be of help for countries and counties deciding on mitigation and reopening strategies. We believe that the results obtained from the proposed method will contribute to societal benefits for a current global concern. äGoing by the numbers : Learning and modeling COVID-19 disease dynamics(epmc).pdf DeepDyve Pubget Overpricing