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10.1016/j.rinp.2021.104306 http://scihub22266oqcxt.onion/10.1016/j.rinp.2021.104306 34002129!8117539!34002129     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34002129&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 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Results+Phys 2021 ; 26 (ä): 104306 Nephropedia Template TP {{tp|p=34002129|t=2021. Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers |pdf=|usr=}}{{34002129}} gab.com Text Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers JO: Results Phys http://www.kidney.de/pget.php?&tw=1&pmid=34002129 #FOAvip This paper examines the predictability of COVID-19 worldwide lethality considering 43 countries. Based on the values inherent to Permutation entropy ( Hs ) and Fisher information measure ( Fs ), we apply the Shannon-Fisher causality plane (SFCP), which allows us to quantify the disorder an evaluate randomness present in the time series of daily death cases related to COVID-19 in each country. We also use Hs and Fs to rank the COVID-19 lethality in these countries based on the complexity hierarchy. Our results suggest that the most proactive countries implemented measures such as facemasks, social distancing, quarantine, massive population testing, and hygienic (sanitary) orientations to limit the impacts of COVID-19, which implied lower entropy (higher predictability) to the COVID-19 lethality. In contrast, the most reactive countries implementing these measures depicted higher entropy (lower predictability) to the COVID-19 lethality. Given this, our findings shed light that these preventive measures are efficient to combat the COVID-19 lethality. Twit Text FOAVip Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers ????Results Phys http://www.kidney.de/pget.php?&tw=1&pmid=34002129 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34002129 #FOAvip English Wikipedia <ref>{{Cite pmid|34002129}}</ref> Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers #MMPMID34002129 Fernandes LHS; Araujo FHA; Silva MAR; Acioli-Santos B Results Phys 2021[Jul]; 26 (ä): 104306 PMID34002129show ga This paper examines the predictability of COVID-19 worldwide lethality considering 43 countries. Based on the values inherent to Permutation entropy ( Hs ) and Fisher information measure ( Fs ), we apply the Shannon-Fisher causality plane (SFCP), which allows us to quantify the disorder an evaluate randomness present in the time series of daily death cases related to COVID-19 in each country. We also use Hs and Fs to rank the COVID-19 lethality in these countries based on the complexity hierarchy. Our results suggest that the most proactive countries implemented measures such as facemasks, social distancing, quarantine, massive population testing, and hygienic (sanitary) orientations to limit the impacts of COVID-19, which implied lower entropy (higher predictability) to the COVID-19 lethality. In contrast, the most reactive countries implementing these measures depicted higher entropy (lower predictability) to the COVID-19 lethality. Given this, our findings shed light that these preventive measures are efficient to combat the COVID-19 lethality. äPredictability of COVID-19 worldwide lethality using permutation-infor(epmc).pdf DeepDyve Pubget Overpricing