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10.1016/j.chaos.2020.110240 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.110240 32863614!7444907!32863614     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32863614&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\32863614.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Chaos+Solitons+Fractals 2020 ; 140 (ä): 110240 Nephropedia Template TP {{tp|p=32863614|t=2020. A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept |pdf=|usr=}}{{32863614}} gab.com Text A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32863614 #FOAvip This article provides predictions for the spread of the SARS-CoV-2 virus for a number of European countries and the United States of America, drawing from their different profiles, both socioeconomically and in terms of outbreak and response to the 2019-2020 coronavirus pandemic, from an engineering and data science perspective. Each country is separately analysed, due to their differences in populations density, cultural habits, health care systems, protective measures, etc. The probabilistic analysis is based on actual data, as provided by the World Health Organization (WHO), as of May 1, 2020. The deployed predictive model provides analytical expressions for the cumulative density function of COVID-19 curve and estimations of the proportion of infected subpopulation for each country. The latter is used to define a Risk Index, towards assessing the level of risk for a country to exhibit high rates of COVID-19 cases after a given interval of observation and given the plans of lifting lockdown measures. Twit Text FOAVip A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32863614 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32863614 #FOAvip English Wikipedia <ref>{{Cite pmid|32863614}}</ref> A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept #MMPMID32863614 Koutsellis T; Nikas A Chaos Solitons Fractals 2020[Nov]; 140 (ä): 110240 PMID32863614show ga This article provides predictions for the spread of the SARS-CoV-2 virus for a number of European countries and the United States of America, drawing from their different profiles, both socioeconomically and in terms of outbreak and response to the 2019-2020 coronavirus pandemic, from an engineering and data science perspective. Each country is separately analysed, due to their differences in populations density, cultural habits, health care systems, protective measures, etc. The probabilistic analysis is based on actual data, as provided by the World Health Organization (WHO), as of May 1, 2020. The deployed predictive model provides analytical expressions for the cumulative density function of COVID-19 curve and estimations of the proportion of infected subpopulation for each country. The latter is used to define a Risk Index, towards assessing the level of risk for a country to exhibit high rates of COVID-19 cases after a given interval of observation and given the plans of lifting lockdown measures. äA predictive model and country risk assessment for COVID-19: An applic(epmc).pdf DeepDyve Pubget Overpricing