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10.3390/ijerph17124540 http://scihub22266oqcxt.onion/10.3390/ijerph17124540 C7344846!7344846!32599746     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 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       Int+J+Environ+Res+Public+Health 2020 ; 17 (12): ä Nephropedia Template TP {{tp|p=32599746|t=2020. Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics |pdf=|usr=}}{{32599746}} gab.com Text Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32599746 #FOAvip The study of infectious disease behavior has been a scientific concern for many years as early identification of outbreaks provides great advantages including timely implementation of public health measures to limit the spread of an epidemic. We propose a methodology that merges the predictions of (i) a computational model with machine learning, (ii) a projection model, and (iii) a proposed smoothed endemic channel calculation. The predictions are made on weekly acute respiratory infection (ARI) data obtained from epidemiological reports in Mexico, along with the usage of key terms in the Google search engine. The results obtained with this methodology were compared with state-of-the-art techniques resulting in reduced root mean squared percentage error (RMPSE) and maximum absolute percent error (MAPE) metrics, achieving a MAPE of 21.7%. This methodology could be extended to detect and raise alerts on possible outbreaks on ARI as well as for other seasonal infectious diseases. Twit Text FOAVip Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32599746 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32599746 #FOAvip English Wikipedia <ref>{{Cite pmid|32599746}}</ref> Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics #MMPMID32599746 Gónzalez-Bandala DA; Cuevas-Tello JC; Noyola DE; Comas-García A; García-Sepúlveda CA Int J Environ Res Public Health 2020[Jun]; 17 (12): ä PMID32599746show ga The study of infectious disease behavior has been a scientific concern for many years as early identification of outbreaks provides great advantages including timely implementation of public health measures to limit the spread of an epidemic. We propose a methodology that merges the predictions of (i) a computational model with machine learning, (ii) a projection model, and (iii) a proposed smoothed endemic channel calculation. The predictions are made on weekly acute respiratory infection (ARI) data obtained from epidemiological reports in Mexico, along with the usage of key terms in the Google search engine. The results obtained with this methodology were compared with state-of-the-art techniques resulting in reduced root mean squared percentage error (RMPSE) and maximum absolute percent error (MAPE) metrics, achieving a MAPE of 21.7%. This methodology could be extended to detect and raise alerts on possible outbreaks on ARI as well as for other seasonal infectious diseases. äComputational Forecasting Methodology for Acute Respiratory Infectious(epmc).pdf DeepDyve Pubget Overpricing