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10.3390/ijerph17197080 http://scihub22266oqcxt.onion/10.3390/ijerph17197080 32992643!7579012!32992643     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 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Environ+Res+Public+Health 2020 ; 17 (19): ä Nephropedia Template TP {{tp|p=32992643|t=2020. Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations |pdf=|usr=}}{{32992643}} gab.com Text Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32992643 #FOAvip The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities. Twit Text FOAVip Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32992643 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32992643 #FOAvip English Wikipedia <ref>{{Cite pmid|32992643}}</ref> Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations #MMPMID32992643 Eltoukhy AEE; Shaban IA; Chan FTS; Abdel-Aal MAM Int J Environ Res Public Health 2020[Sep]; 17 (19): ä PMID32992643show ga The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities. |*Data Science[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology[MESH] |Forecasting/methods[MESH] |Global Health/*statistics & numerical data[MESH] |Humans[MESH] |Pandemics[MESH]Data Analytics for Predicting COVID-19 Cases in Top Affected Countries(epmc).pdf DeepDyve Pubget Overpricing