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10.1016/j.chaos.2020.110150 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.110150 32834638!7386499!32834638     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32834638&cmd=llinks): Failed to open stream: HTTP request failed! 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Analysis of the evolution of the Sars-Cov-2 in Italy, the role of the asymptomatics and the success of Logistic model |pdf=|usr=}}{{32834638}} gab.com Text Analysis of the evolution of the Sars-Cov-2 in Italy, the role of the asymptomatics and the success of Logistic model JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834638 #FOAvip In this article we study the temporal evolution of the pandemic Sars-Cov-2 in Italy by means of dynamic population models. The time window of the available population data is between February 24, and March 25. After we upgrade the data until April 1. We perform the analysis with 4 different models and we think that the best candidate to correctly described the italian situation is a generalized Logistic equation. We use two coupled differential equations that model the evolution of the severe infected and the dead. This choice is due to the fact that in Italy the pharyngeal swabs are made only to severe infected, therefore we have no information about asymptomatic people. Moreover, an important observation is that the virus spreads between Regions with some delay. Indeed, we suggest that a different analysis, region by region, would be more sensible than one on the whole Italy. In particular the region Lombardy has a behaviour very fast compared to the other ones. We show the fit and forecast of the dead and total severe infected for Italy and five regions: Lombardy, Piedmont, Emilia-Romagna, Veneto and Tuscany. Finally we perform an analysis of the peak (intended, in our study, as the maximum of the daily total severe infected) and an estimation of how many lives have been saved by means of the LockDown. Twit Text FOAVip Analysis of the evolution of the Sars-Cov-2 in Italy, the role of the asymptomatics and the success of Logistic model ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834638 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32834638 #FOAvip English Wikipedia <ref>{{Cite pmid|32834638}}</ref> Analysis of the evolution of the Sars-Cov-2 in Italy, the role of the asymptomatics and the success of Logistic model #MMPMID32834638 Martelloni G; Martelloni G Chaos Solitons Fractals 2020[Nov]; 140 (ä): 110150 PMID32834638show ga In this article we study the temporal evolution of the pandemic Sars-Cov-2 in Italy by means of dynamic population models. The time window of the available population data is between February 24, and March 25. After we upgrade the data until April 1. We perform the analysis with 4 different models and we think that the best candidate to correctly described the italian situation is a generalized Logistic equation. We use two coupled differential equations that model the evolution of the severe infected and the dead. This choice is due to the fact that in Italy the pharyngeal swabs are made only to severe infected, therefore we have no information about asymptomatic people. Moreover, an important observation is that the virus spreads between Regions with some delay. Indeed, we suggest that a different analysis, region by region, would be more sensible than one on the whole Italy. In particular the region Lombardy has a behaviour very fast compared to the other ones. We show the fit and forecast of the dead and total severe infected for Italy and five regions: Lombardy, Piedmont, Emilia-Romagna, Veneto and Tuscany. Finally we perform an analysis of the peak (intended, in our study, as the maximum of the daily total severe infected) and an estimation of how many lives have been saved by means of the LockDown. äAnalysis of the evolution of the Sars-Cov-2 in Italy, the role of the (epmc).pdf DeepDyve Pubget Overpricing