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10.1371/journal.pone.0237417 http://scihub22266oqcxt.onion/10.1371/journal.pone.0237417 32760133!7410324!32760133     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       PLoS+One 2020 ; 15 (8): e0237417 Nephropedia Template TP {{tp|p=32760133|t=2020. Monitoring Italian COVID-19 spread by a forced SEIRD model |pdf=|usr=}}{{32760133}} gab.com Text Monitoring Italian COVID-19 spread by a forced SEIRD model JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32760133 #FOAvip Due to the recent evolution of the COVID-19 outbreak, the scientific community is making efforts to analyse models for understanding the present situation and for predicting future scenarios. In this paper, we propose a forced Susceptible-Exposed-Infected-Recovered-Dead (fSEIRD) differential model for the analysis and forecast of the COVID-19 spread in Italian regions, using the data from the Italian Protezione Civile (Italian Civil Protection Department) from 24/02/2020. In this study, we investigate an adaptation of fSEIRD by proposing two different piecewise time-dependent infection rate functions to fit the current epidemic data affected by progressive movement restriction policies put in place by the Italian government. The proposed models are flexible and can be quickly adapted to monitor various epidemic scenarios. Results on the regions of Lombardia and Emilia-Romagna confirm that the proposed models fit the data very accurately and make reliable predictions. Twit Text FOAVip Monitoring Italian COVID-19 spread by a forced SEIRD model ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32760133 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32760133 #FOAvip English Wikipedia <ref>{{Cite pmid|32760133}}</ref> Monitoring Italian COVID-19 spread by a forced SEIRD model #MMPMID32760133 Loli Piccolomini E; Zama F PLoS One 2020[]; 15 (8): e0237417 PMID32760133show ga Due to the recent evolution of the COVID-19 outbreak, the scientific community is making efforts to analyse models for understanding the present situation and for predicting future scenarios. In this paper, we propose a forced Susceptible-Exposed-Infected-Recovered-Dead (fSEIRD) differential model for the analysis and forecast of the COVID-19 spread in Italian regions, using the data from the Italian Protezione Civile (Italian Civil Protection Department) from 24/02/2020. In this study, we investigate an adaptation of fSEIRD by proposing two different piecewise time-dependent infection rate functions to fit the current epidemic data affected by progressive movement restriction policies put in place by the Italian government. The proposed models are flexible and can be quickly adapted to monitor various epidemic scenarios. Results on the regions of Lombardia and Emilia-Romagna confirm that the proposed models fit the data very accurately and make reliable predictions. |*Models, Statistical[MESH] |Betacoronavirus/isolation & purification[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology/pathology/transmission/virology[MESH] |Disease Outbreaks[MESH] |Humans[MESH] |Italy/epidemiology[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology/pathology/transmission/virology[MESH]Monitoring Italian COVID-19 spread by a forced SEIRD model (epmc).pdf DeepDyve Pubget Overpricing