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10.1016/j.dsx.2020.07.045 http://scihub22266oqcxt.onion/10.1016/j.dsx.2020.07.045 32771920!7395225!32771920     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Diabetes+Metab+Syndr 2020 ; 14 (5): 1467-1474 Nephropedia Template TP {{tp|p=32771920|t=2020. Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model |pdf=|usr=}}{{32771920}} gab.com Text Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model JO: Diabetes Metab Syndr http://www.kidney.de/pget.php?&tw=1&pmid=32771920 #FOAvip INTRODUCTION AND AIMS: The COVID-19 pandemic originated from the city of Wuhan of China has highly affected the health, socio-economic and financial matters of the different countries of the world. India is one of the countries which is affected by the disease and thousands of people on daily basis are getting infected. In this paper, an analysis of daily statistics of people affected by the disease are taken into account to predict the next days trend in the active cases in Odisha as well as India. MATERIAL AND METHODS: A valid global data set is collected from the WHO daily statistics and correlation among the total confirmed, active, deceased, positive cases are stated in this paper. Regression model such as Linear and Multiple Linear Regression techniques are applied to the data set to visualize the trend of the affected cases. RESULTS: Here a comparison of Linear Regression and Multiple Linear Regression model is performed where the score of the model R(2)(tends to be 0.99 and 1.0 which indicates a strong prediction model to forecast the next coming days active cases. Using the Multiple Linear Regression model as on July month, the forecast value of 52,290 active cases are predicted towards the next month of 15th August in India and 9,358 active cases in Odisha if situation continues like this way.) CONCLUSION: These models acquired remarkable accuracy in COVID-19 recognition. A strong correlation factor determines the relationship among the dependent (active) with the independent variables (positive, deceased, recovered). Twit Text FOAVip Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model ????Diabetes Metab Syndr http://www.kidney.de/pget.php?&tw=1&pmid=32771920 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32771920 #FOAvip English Wikipedia <ref>{{Cite pmid|32771920}}</ref> Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model #MMPMID32771920 Rath S; Tripathy A; Tripathy AR Diabetes Metab Syndr 2020[Sep]; 14 (5): 1467-1474 PMID32771920show ga INTRODUCTION AND AIMS: The COVID-19 pandemic originated from the city of Wuhan of China has highly affected the health, socio-economic and financial matters of the different countries of the world. India is one of the countries which is affected by the disease and thousands of people on daily basis are getting infected. In this paper, an analysis of daily statistics of people affected by the disease are taken into account to predict the next days trend in the active cases in Odisha as well as India. MATERIAL AND METHODS: A valid global data set is collected from the WHO daily statistics and correlation among the total confirmed, active, deceased, positive cases are stated in this paper. Regression model such as Linear and Multiple Linear Regression techniques are applied to the data set to visualize the trend of the affected cases. RESULTS: Here a comparison of Linear Regression and Multiple Linear Regression model is performed where the score of the model R(2)(tends to be 0.99 and 1.0 which indicates a strong prediction model to forecast the next coming days active cases. Using the Multiple Linear Regression model as on July month, the forecast value of 52,290 active cases are predicted towards the next month of 15th August in India and 9,358 active cases in Odisha if situation continues like this way.) CONCLUSION: These models acquired remarkable accuracy in COVID-19 recognition. A strong correlation factor determines the relationship among the dependent (active) with the independent variables (positive, deceased, recovered). |*Linear Models[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology[MESH] |Forecasting[MESH] |Humans[MESH] |India/epidemiology[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH]Prediction of new active cases of coronavirus disease (COVID-19) pande(epmc).pdf DeepDyve Pubget Overpricing