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10.1016/j.dib.2020.105854 http://scihub22266oqcxt.onion/10.1016/j.dib.2020.105854 32572378!7292003!32572378     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Data+Brief 2020 ; 31 (ä): 105854 Nephropedia Template TP {{tp|p=32572378|t=2020. Using the kalman filter with Arima for the COVID-19 pandemic dataset of Pakistan |pdf=|usr=}}{{32572378}} gab.com Text Using the kalman filter with Arima for the COVID-19 pandemic dataset of Pakistan JO: Data Brief http://www.kidney.de/pget.php?&tw=1&pmid=32572378 #FOAvip The current pandemic of the Novel Corona virus (COVID-19) has resulted in multifold challenges related to health, economy, and society, etc. for the entire world. Many mathematical epidemiological models have been tried for the available data of the COVID-19 pandemic with the core objective to observe the trend and trajectories of infected cases, recoveries, and deaths, etc. However, these models have their own assumptions and parameters and vary with regional demography. This article suggests the use of a more pragmatic approach of the Kalman filter with the Autoregressive Integrated Moving Average (ARIMA) models in order to obtain more precise forecasts for the figures of prevalence, active cases, recoveries, and deaths related to the COVID-19 outbreak in Pakistan. Twit Text FOAVip Using the kalman filter with Arima for the COVID-19 pandemic dataset of Pakistan ????Data Brief http://www.kidney.de/pget.php?&tw=1&pmid=32572378 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32572378 #FOAvip English Wikipedia <ref>{{Cite pmid|32572378}}</ref> Using the kalman filter with Arima for the COVID-19 pandemic dataset of Pakistan #MMPMID32572378 Aslam M Data Brief 2020[Aug]; 31 (ä): 105854 PMID32572378show ga The current pandemic of the Novel Corona virus (COVID-19) has resulted in multifold challenges related to health, economy, and society, etc. for the entire world. Many mathematical epidemiological models have been tried for the available data of the COVID-19 pandemic with the core objective to observe the trend and trajectories of infected cases, recoveries, and deaths, etc. However, these models have their own assumptions and parameters and vary with regional demography. This article suggests the use of a more pragmatic approach of the Kalman filter with the Autoregressive Integrated Moving Average (ARIMA) models in order to obtain more precise forecasts for the figures of prevalence, active cases, recoveries, and deaths related to the COVID-19 outbreak in Pakistan. äUsing the kalman filter with Arima for the COVID-19 pandemic dataset o(epmc).pdf DeepDyve Pubget Overpricing