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10.1111/tbed.13764 http://scihub22266oqcxt.onion/10.1111/tbed.13764 32749759!7436699!32749759     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 294.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Transbound+Emerg+Dis 2021 ; 68 (3): 1001-1018 Nephropedia Template TP {{tp|p=32749759|t=2021. Exploring the growth of COVID-19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning |pdf=|usr=}}{{32749759}} gab.com Text Exploring the growth of COVID-19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning JO: Transbound Emerg Dis http://www.kidney.de/pget.php?&tw=1&pmid=32749759 #FOAvip The coronavirus disease 2019 (COVID-19) pandemic spread by the single-stranded RNA severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to the seventh generation of the coronavirus family. Following an unusual replication mechanism, its extreme ease of transmissivity has put many countries under lockdown. With the uncertainty of developing a cure/vaccine for the infection in the near future, the onus currently lies on healthcare infrastructure, policies, government activities, and behaviour of the people to contain the virus. This research uses exponential growth modelling studies to understand the spreading patterns of SARS-CoV-2 and identifies countries that showed early signs of containment until March 26, 2020. Predictive supervised machine learning models are built using infrastructure, environment, policies, and infection-related independent variables to predict early containment. COVID-19 infection data across 42 countries are used. Logistic regression results show a positive significant relationship between healthcare infrastructure and lockdown policies, and signs of early containment. Machine learning models based on logistic regression, decision tree, random forest, and support vector machines are developed and show accuracies between 76.2% and 92.9% to predict early signs of infection containment. Other policies and the decisions taken by countries to contain the infection are also discussed. Twit Text FOAVip Exploring the growth of COVID-19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning ????Transbound Emerg Dis http://www.kidney.de/pget.php?&tw=1&pmid=32749759 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32749759 #FOAvip English Wikipedia <ref>{{Cite pmid|32749759}}</ref> Exploring the growth of COVID-19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning #MMPMID32749759 Kasilingam D; Sathiya Prabhakaran SP; Rajendran DK; Rajagopal V; Santhosh Kumar T; Soundararaj A Transbound Emerg Dis 2021[May]; 68 (3): 1001-1018 PMID32749759show ga The coronavirus disease 2019 (COVID-19) pandemic spread by the single-stranded RNA severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to the seventh generation of the coronavirus family. Following an unusual replication mechanism, its extreme ease of transmissivity has put many countries under lockdown. With the uncertainty of developing a cure/vaccine for the infection in the near future, the onus currently lies on healthcare infrastructure, policies, government activities, and behaviour of the people to contain the virus. This research uses exponential growth modelling studies to understand the spreading patterns of SARS-CoV-2 and identifies countries that showed early signs of containment until March 26, 2020. Predictive supervised machine learning models are built using infrastructure, environment, policies, and infection-related independent variables to predict early containment. COVID-19 infection data across 42 countries are used. Logistic regression results show a positive significant relationship between healthcare infrastructure and lockdown policies, and signs of early containment. Machine learning models based on logistic regression, decision tree, random forest, and support vector machines are developed and show accuracies between 76.2% and 92.9% to predict early signs of infection containment. Other policies and the decisions taken by countries to contain the infection are also discussed. |*Machine Learning[MESH] |*Models, Biological[MESH] |*SARS-CoV-2[MESH] |Animals[MESH] |COVID-19/*epidemiology[MESH] |Communicable Disease Control/*statistics & numerical data[MESH] |Humans[MESH]Exploring the growth of COVID-19 cases using exponential modelling acr(epmc).pdf DeepDyve Pubget Overpricing