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10.1007/s11071-020-05757-6 http://scihub22266oqcxt.onion/10.1007/s11071-020-05757-6 C7314430!7314430!32836806     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nonlinear+Dyn 2020 ; 101 (1): 711-8 Nephropedia Template TP {{tp|p=32836806|t=2020. A fractional-order model for the novel coronavirus (COVID-19) outbreak |pdf=|usr=}}{{32836806}} gab.com Text A fractional-order model for the novel coronavirus (COVID-19) outbreak JO: Nonlinear Dyn http://www.kidney.de/pget.php?&tw=1&pmid=32836806 #FOAvip The outbreak of the novel coronavirus (COVID-19), which was firstly reported in China, has affected many countries worldwide. To understand and predict the transmission dynamics of this disease, mathematical models can be very effective. It has been shown that the fractional order is related to the memory effects, which seems to be more effective for modeling the epidemic diseases. Motivated by this, in this paper, we propose fractional-order susceptible individuals, asymptomatic infected, symptomatic infected, recovered, and deceased (SEIRD) model for the spread of COVID-19. We consider both classical and fractional-order models and estimate the parameters by using the real data of Italy, reported by the World Health Organization. The results show that the fractional-order model has less root-mean-square error than the classical one. Finally, the prediction ability of both of the integer- and fractional-order models is evaluated by using a test data set. The results show that the fractional model provides a closer forecast to the real data. Twit Text FOAVip A fractional-order model for the novel coronavirus (COVID-19) outbreak ????Nonlinear Dyn http://www.kidney.de/pget.php?&tw=1&pmid=32836806 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32836806 #FOAvip English Wikipedia <ref>{{Cite pmid|32836806}}</ref> A fractional-order model for the novel coronavirus (COVID-19) outbreak #MMPMID32836806 Rajagopal K; Hasanzadeh N; Parastesh F; Hamarash II; Jafari S; Hussain I Nonlinear Dyn 2020[]; 101 (1): 711-8 PMID32836806show ga The outbreak of the novel coronavirus (COVID-19), which was firstly reported in China, has affected many countries worldwide. To understand and predict the transmission dynamics of this disease, mathematical models can be very effective. It has been shown that the fractional order is related to the memory effects, which seems to be more effective for modeling the epidemic diseases. Motivated by this, in this paper, we propose fractional-order susceptible individuals, asymptomatic infected, symptomatic infected, recovered, and deceased (SEIRD) model for the spread of COVID-19. We consider both classical and fractional-order models and estimate the parameters by using the real data of Italy, reported by the World Health Organization. The results show that the fractional-order model has less root-mean-square error than the classical one. Finally, the prediction ability of both of the integer- and fractional-order models is evaluated by using a test data set. The results show that the fractional model provides a closer forecast to the real data. äA fractional-order model for the novel coronavirus (COVID-19) outbreak(epmc).pdf DeepDyve Pubget Overpricing