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10.1016/j.ins.2021.06.011 http://scihub22266oqcxt.onion/10.1016/j.ins.2021.06.011 34127869!8188773!34127869     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34127869&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Inf+Sci+(N+Y) 2021 ; 574 (ä): 333-348 Nephropedia Template TP {{tp|p=34127869|t=2021. Inferring statistical trends of the COVID19 pandemic from current data Where probability meets fuzziness |pdf=|usr=}}{{34127869}} gab.com Text Inferring statistical trends of the COVID19 pandemic from current data Where probability meets fuzziness JO: Inf Sci (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=34127869 #FOAvip We introduce unprecedented tools to infer approximate evolution features of the COVID19 outbreak when these features are altered by containment measures. In this framework we present: (1) a basic tool to deal with samples that are both truncated and non independently drawn, and (2) a two-phase random variable to capture a game changer along a process evolution. To overcome these challenges we lie in an intermediate domain between probability models and fuzzy sets, still maintaining probabilistic features of the employed statistics as the reference KPI of the tools. This research uses as a benchmark the daily cumulative death numbers of COVID19 in two countries, with no any ancillary data. Numerical results show: (i) the model capability of capturing the inflection point and forecasting the end-of-infection time and related outbreak size, and (ii) the out-performance of the model inference method according to conventional indicators. Twit Text FOAVip Inferring statistical trends of the COVID19 pandemic from current data Where probability meets fuzziness ????Inf Sci (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=34127869 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34127869 #FOAvip English Wikipedia <ref>{{Cite pmid|34127869}}</ref> Inferring statistical trends of the COVID19 pandemic from current data Where probability meets fuzziness #MMPMID34127869 Apolloni B Inf Sci (N Y) 2021[Oct]; 574 (ä): 333-348 PMID34127869show ga We introduce unprecedented tools to infer approximate evolution features of the COVID19 outbreak when these features are altered by containment measures. In this framework we present: (1) a basic tool to deal with samples that are both truncated and non independently drawn, and (2) a two-phase random variable to capture a game changer along a process evolution. To overcome these challenges we lie in an intermediate domain between probability models and fuzzy sets, still maintaining probabilistic features of the employed statistics as the reference KPI of the tools. This research uses as a benchmark the daily cumulative death numbers of COVID19 in two countries, with no any ancillary data. Numerical results show: (i) the model capability of capturing the inflection point and forecasting the end-of-infection time and related outbreak size, and (ii) the out-performance of the model inference method according to conventional indicators. äInferring statistical trends of the COVID19 pandemic from current data(epmc).pdf DeepDyve Pubget Overpricing