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10.1016/j.csbj.2020.09.015 http://scihub22266oqcxt.onion/10.1016/j.csbj.2020.09.015 32994886!7513749!32994886     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 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       Comput+Struct+Biotechnol+J 2020 ; 18 (ä): 2972-3206 Nephropedia Template TP {{tp|p=32994886|t=2020. Tailoring time series models for forecasting coronavirus spread: Case studies of 187 countries |pdf=|usr=}}{{32994886}} gab.com Text Tailoring time series models for forecasting coronavirus spread: Case studies of 187 countries JO: Comput Struct Biotechnol J http://www.kidney.de/pget.php?&tw=1&pmid=32994886 #FOAvip When will the coronavirus end? Are the current precautionary measures effective? To answer these questions it is important to forecast regularly and accurately the spread of COVID-19 infections. Different time series forecasting models have been applied in the literature to tackle the pandemic situation. The current research efforts developed few of these models and validates its accuracy for selected countries. It becomes difficult to draw an objective comparison between the performance of these models at a global scale. This is because, the time series trend for the infection differs between the countries depending on the strategies adopted by the healthcare organizations to decrease the spread. Consequently, it is important to develop a tailored model for a country that allows healthcare organizations to better judge the effect of the undertaken precautionary measures, and provision more efficiently the needed resources to face this disease. This paper addresses this void. We develop and compare the performance of the time series models in the literature in terms of root mean squared error and mean absolute percentage error. Twit Text FOAVip Tailoring time series models for forecasting coronavirus spread: Case studies of 187 countries ????Comput Struct Biotechnol J http://www.kidney.de/pget.php?&tw=1&pmid=32994886 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32994886 #FOAvip English Wikipedia <ref>{{Cite pmid|32994886}}</ref> Tailoring time series models for forecasting coronavirus spread: Case studies of 187 countries #MMPMID32994886 Ismail L; Materwala H; Znati T; Turaev S; Khan MAB Comput Struct Biotechnol J 2020[]; 18 (ä): 2972-3206 PMID32994886show ga When will the coronavirus end? Are the current precautionary measures effective? To answer these questions it is important to forecast regularly and accurately the spread of COVID-19 infections. Different time series forecasting models have been applied in the literature to tackle the pandemic situation. The current research efforts developed few of these models and validates its accuracy for selected countries. It becomes difficult to draw an objective comparison between the performance of these models at a global scale. This is because, the time series trend for the infection differs between the countries depending on the strategies adopted by the healthcare organizations to decrease the spread. Consequently, it is important to develop a tailored model for a country that allows healthcare organizations to better judge the effect of the undertaken precautionary measures, and provision more efficiently the needed resources to face this disease. This paper addresses this void. We develop and compare the performance of the time series models in the literature in terms of root mean squared error and mean absolute percentage error. äTailoring time series models for forecasting coronavirus spread: Case (epmc).pdf DeepDyve Pubget Overpricing