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10.1371/journal.pone.0123166 http://scihub22266oqcxt.onion/10.1371/journal.pone.0123166 C4395290!4395290!25875211     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2015 ; 10 (4): ä Nephropedia Template TP {{tp|p=25875211|t=2015. Modeling and Predicting Hemorrhagic Fever with Renal Syndrome Trends Based on Meteorological Factors in Hu County, China |pdf=|usr=}}{{25875211}} gab.com Text Modeling and Predicting Hemorrhagic Fever with Renal Syndrome Trends Based on Meteorological Factors in Hu County, China JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=25875211 #FOAvip Background: Hu County is a serious hemorrhagic fever with renal syndrome (HFRS) epidemic area, with notable fluctuation of the HFRS epidemic in recent years. This study aimed to explore the optimal model for HFRS epidemic prediction in Hu. Methods: Three models were constructed and compared, including a generalized linear model (GLM), a generalized additive model (GAM), and a principal components regression model (PCRM). The fitting and predictive adjusted R2 of each model were calculated. Ljung-Box Q tests for fitted and predicted residuals of each model were conducted. The study period was stratified into before (1971?1993) and after (1994?2012) vaccine implementation epochs to avoid the confounding factor of vaccination. Results: The autocorrelation of fitted and predicted residuals of the GAM in the two epochs were not significant (Ljung-Box Q test, P>.05). The adjusted R2 for the predictive abilities of the GLM, GAM, and PCRM were 0.752, 0.799, and 0.665 in the early epoch, and 0.669, 0.756, and 0.574 in the recent epoch. The adjusted R2 values of the three models were lower in the early epoch than in the recent epoch. Conclusions: GAM is superior to GLM and PCRM for monthly HFRS case number prediction in Hu County. A shift in model reliability coincident with vaccination implementation demonstrates the importance of vaccination in HFRS control and prevention. Twit Text FOAVip Modeling and Predicting Hemorrhagic Fever with Renal Syndrome Trends Based on Meteorological Factors in Hu County, China ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=25875211 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25875211 #FOAvip English Wikipedia <ref>{{Cite pmid|25875211}}</ref> Modeling and Predicting Hemorrhagic Fever with Renal Syndrome Trends Based on Meteorological Factors in Hu County, China #MMPMID25875211 Xiao D; Wu K; Tan X; Le J; Li H; Yan Y; Xu Z PLoS One 2015[]; 10 (4): ä PMID25875211show ga Background: Hu County is a serious hemorrhagic fever with renal syndrome (HFRS) epidemic area, with notable fluctuation of the HFRS epidemic in recent years. This study aimed to explore the optimal model for HFRS epidemic prediction in Hu. Methods: Three models were constructed and compared, including a generalized linear model (GLM), a generalized additive model (GAM), and a principal components regression model (PCRM). The fitting and predictive adjusted R2 of each model were calculated. Ljung-Box Q tests for fitted and predicted residuals of each model were conducted. The study period was stratified into before (1971?1993) and after (1994?2012) vaccine implementation epochs to avoid the confounding factor of vaccination. Results: The autocorrelation of fitted and predicted residuals of the GAM in the two epochs were not significant (Ljung-Box Q test, P>.05). The adjusted R2 for the predictive abilities of the GLM, GAM, and PCRM were 0.752, 0.799, and 0.665 in the early epoch, and 0.669, 0.756, and 0.574 in the recent epoch. The adjusted R2 values of the three models were lower in the early epoch than in the recent epoch. Conclusions: GAM is superior to GLM and PCRM for monthly HFRS case number prediction in Hu County. A shift in model reliability coincident with vaccination implementation demonstrates the importance of vaccination in HFRS control and prevention. äModeling and Predicting Hemorrhagic Fever with Renal Syndrome Trends B(epmc).pdf DeepDyve Pubget Overpricing