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10.3390/ijerph17072365 http://scihub22266oqcxt.onion/10.3390/ijerph17072365 32244425!7177617!32244425     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 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 (7): ä Nephropedia Template TP {{tp|p=32244425|t=2020. Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index |pdf=|usr=}}{{32244425}} gab.com Text Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32244425 #FOAvip Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series of SMSI to predict new suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new suspected COVID-19 case numbers from 20 January 2020 to 9 February 2020. We further validated the optimal method for new confirmed cases of COVID-19 from 31 December 2019 to 17 February 2020. The new suspected COVID-19 case numbers correlated significantly with the lagged series of SMSI. SMSI could be detected 6-9 days earlier than new suspected cases of COVID-19. The optimal method was the subset selection method, which had the lowest estimation error and a moderate number of predictors. The subset selection method also significantly correlated with the new confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new confirmed COVID-19 cases. SMSI could be a significant predictor of the number of COVID-19 infections. SMSI could be an effective early predictor, which would enable governments' health departments to locate potential and high-risk outbreak areas. Twit Text FOAVip Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32244425 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32244425 #FOAvip English Wikipedia <ref>{{Cite pmid|32244425}}</ref> Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index #MMPMID32244425 Qin L; Sun Q; Wang Y; Wu KF; Chen M; Shia BC; Wu SY Int J Environ Res Public Health 2020[Mar]; 17 (7): ä PMID32244425show ga Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series of SMSI to predict new suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new suspected COVID-19 case numbers from 20 January 2020 to 9 February 2020. We further validated the optimal method for new confirmed cases of COVID-19 from 31 December 2019 to 17 February 2020. The new suspected COVID-19 case numbers correlated significantly with the lagged series of SMSI. SMSI could be detected 6-9 days earlier than new suspected cases of COVID-19. The optimal method was the subset selection method, which had the lowest estimation error and a moderate number of predictors. The subset selection method also significantly correlated with the new confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new confirmed COVID-19 cases. SMSI could be a significant predictor of the number of COVID-19 infections. SMSI could be an effective early predictor, which would enable governments' health departments to locate potential and high-risk outbreak areas. |*Coronavirus Infections/complications/epidemiology/prevention & control[MESH] |*Data Mining[MESH] |*Pandemics/prevention & control[MESH] |*Pneumonia, Viral/complications/epidemiology/prevention & control[MESH] |*Social Media/statistics & numerical data[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Computer Simulation[MESH] |Coronavirus[MESH] |Cough/epidemiology/etiology[MESH] |Disease Outbreaks/prevention & control[MESH] |Dyspnea/epidemiology/etiology[MESH] |Fever/epidemiology/etiology[MESH] |Forecasting[MESH] |Humans[MESH] |Pneumonia/epidemiology/etiology[MESH] |Risk Assessment[MESH] |SARS-CoV-2[MESH]Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Usi(epmc).pdf DeepDyve Pubget Overpricing