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10.1371/journal.pone.0234763 http://scihub22266oqcxt.onion/10.1371/journal.pone.0234763 32628673!7337285!32628673     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2020 ; 15 (7): e0234763 Nephropedia Template TP {{tp|p=32628673|t=2020. Prediction of COVID-19 spreading profiles in South Korea, Italy and Iran by data-driven coding |pdf=|usr=}}{{32628673}} gab.com Text Prediction of COVID-19 spreading profiles in South Korea, Italy and Iran by data-driven coding JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32628673 #FOAvip This work applies a data-driven coding method for prediction of the COVID-19 spreading profile in any given population that shows an initial phase of epidemic progression. Based on the historical data collected for COVID-19 spreading in 367 cities in China and the set of parameters of the augmented Susceptible-Exposed-Infected-Removed (SEIR) model obtained for each city, a set of profile codes representing a variety of transmission mechanisms and contact topologies is formed. By comparing the data of an early outbreak of a given population with the complete set of historical profiles, the best fit profiles are selected and the corresponding sets of profile codes are used for prediction of the future progression of the epidemic in that population. Application of the method to the data collected for South Korea, Italy and Iran shows that peaks of infection cases are expected to occur before mid April, the end of March and the end of May 2020, and that the percentage of population infected in each city or region will be less than 0.01%, 0.5% and 0.5%, for South Korea, Italy and Iran, respectively. Twit Text FOAVip Prediction of COVID-19 spreading profiles in South Korea, Italy and Iran by data-driven coding ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32628673 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32628673 #FOAvip English Wikipedia <ref>{{Cite pmid|32628673}}</ref> Prediction of COVID-19 spreading profiles in South Korea, Italy and Iran by data-driven coding #MMPMID32628673 Zhan C; Tse CK; Lai Z; Hao T; Su J PLoS One 2020[]; 15 (7): e0234763 PMID32628673show ga This work applies a data-driven coding method for prediction of the COVID-19 spreading profile in any given population that shows an initial phase of epidemic progression. Based on the historical data collected for COVID-19 spreading in 367 cities in China and the set of parameters of the augmented Susceptible-Exposed-Infected-Removed (SEIR) model obtained for each city, a set of profile codes representing a variety of transmission mechanisms and contact topologies is formed. By comparing the data of an early outbreak of a given population with the complete set of historical profiles, the best fit profiles are selected and the corresponding sets of profile codes are used for prediction of the future progression of the epidemic in that population. Application of the method to the data collected for South Korea, Italy and Iran shows that peaks of infection cases are expected to occur before mid April, the end of March and the end of May 2020, and that the percentage of population infected in each city or region will be less than 0.01%, 0.5% and 0.5%, for South Korea, Italy and Iran, respectively. |*Betacoronavirus[MESH] |COVID-19[MESH] |Cities/epidemiology[MESH] |Coronavirus Infections/diagnosis/*epidemiology/*transmission/virology[MESH] |Data Science/*methods[MESH] |Humans[MESH] |Iran/epidemiology[MESH] |Italy/epidemiology[MESH] |Models, Statistical[MESH] |Pandemics[MESH] |Pneumonia, Viral/diagnosis/*epidemiology/*transmission/virology[MESH] |Prognosis[MESH] |Republic of Korea/epidemiology[MESH]Prediction of COVID-19 spreading profiles in South Korea, Italy and Ir(epmc).pdf DeepDyve Pubget Overpricing