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10.1016/j.idm.2020.03.001 http://scihub22266oqcxt.onion/10.1016/j.idm.2020.03.001 32289100!7104073!32289100     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       Infect+Dis+Model 2020 ; 5 (ä): 271-281 Nephropedia Template TP {{tp|p=32289100|t=2020. Why is it difficult to accurately predict the COVID-19 epidemic?|pdf=|usr=}}{{32289100}} gab.com Text Why is it difficult to accurately predict the COVID-19 epidemic JO: Infect Dis Model http://www.kidney.de/pget.php?&tw=1&pmid=32289100 #FOAvip Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city. Twit Text FOAVip Why is it difficult to accurately predict the COVID-19 epidemic ????Infect Dis Model http://www.kidney.de/pget.php?&tw=1&pmid=32289100 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32289100 #FOAvip English Wikipedia <ref>{{Cite pmid|32289100}}</ref> Why is it difficult to accurately predict the COVID-19 epidemic? #MMPMID32289100 Roda WC; Varughese MB; Han D; Li MY Infect Dis Model 2020[]; 5 (ä): 271-281 PMID32289100show ga Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city. äWhy is it difficult to accurately predict the COVID-19 epidemic?(epmc).pdf DeepDyve Pubget Overpricing