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10.1016/j.matpr.2021.01.524 http://scihub22266oqcxt.onion/10.1016/j.matpr.2021.01.524 33532246!7843033!33532246     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 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       Mater+Today+Proc 2021 ; ä (ä): ä Nephropedia Template TP {{tp|p=33532246|t=2021. The Impact of Various Policy Factors Implemented for Controlling the Spread of COVID-19 |pdf=|usr=}}{{33532246}} gab.com Text The Impact of Various Policy Factors Implemented for Controlling the Spread of COVID-19 JO: Mater Today Proc http://www.kidney.de/pget.php?&tw=1&pmid=33532246 #FOAvip More than sixty million cases were affected by the novel corona virus around the world till date. The virus has reached more than 200 countries and more than seven lakh people have lost their lives globally so far. To control the spread of this virus many countries have taken extreme measures but still couldn't control the spread. The primary objective of this analysis is to classify the various policy factors adopted by the countries to manage the spread of Covid-19. Our study uses Oxford Covid-19 Government Response Tracker (OxCGRT) dataset and Autoregressive Integrated Moving Average (ARIMA) model as the model for forecasting. The representation is trained using day wise number of infected cases reported in each country from August'2020 to October'2020 and then forecasts the number of infections for five days from 15(th) November' 2020 to 19(th) November'2020. We have included 15 countries in our study and analysed 13 factors which includes 8 factors in Containment and Closure policies category, 2 factors in Economic policies category and 3 factors in Health System policies category. We analysed the impact of above factors by comparing the forecasted number of affected people with the actual total diseased cases reported in those five days. The study discovers the fact that out of thirteen policy factors, the countries which concentrated more on policies in economic category during the pandemic have helped in controlling the dissemination of covid-19. Twit Text FOAVip The Impact of Various Policy Factors Implemented for Controlling the Spread of COVID-19 ????Mater Today Proc http://www.kidney.de/pget.php?&tw=1&pmid=33532246 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33532246 #FOAvip English Wikipedia <ref>{{Cite pmid|33532246}}</ref> The Impact of Various Policy Factors Implemented for Controlling the Spread of COVID-19 #MMPMID33532246 Senthil Prakash PN; Hariharan B; Kaliraj S; Siva R; Vivek D Mater Today Proc 2021[Jan]; ä (ä): ä PMID33532246show ga More than sixty million cases were affected by the novel corona virus around the world till date. The virus has reached more than 200 countries and more than seven lakh people have lost their lives globally so far. To control the spread of this virus many countries have taken extreme measures but still couldn't control the spread. The primary objective of this analysis is to classify the various policy factors adopted by the countries to manage the spread of Covid-19. Our study uses Oxford Covid-19 Government Response Tracker (OxCGRT) dataset and Autoregressive Integrated Moving Average (ARIMA) model as the model for forecasting. The representation is trained using day wise number of infected cases reported in each country from August'2020 to October'2020 and then forecasts the number of infections for five days from 15(th) November' 2020 to 19(th) November'2020. We have included 15 countries in our study and analysed 13 factors which includes 8 factors in Containment and Closure policies category, 2 factors in Economic policies category and 3 factors in Health System policies category. We analysed the impact of above factors by comparing the forecasted number of affected people with the actual total diseased cases reported in those five days. The study discovers the fact that out of thirteen policy factors, the countries which concentrated more on policies in economic category during the pandemic have helped in controlling the dissemination of covid-19. äThe Impact of Various Policy Factors Implemented for Controlling the S(epmc).pdf DeepDyve Pubget Overpricing