Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1371/journal.pone.0243123 http://scihub22266oqcxt.onion/10.1371/journal.pone.0243123 33290420!7723280!33290420     free     free     free       PLoS+One 2020 ; 15 (12): e0243123 Nephropedia Template TP {{tp|p=33290420|t=2020. On the authenticity of COVID-19 case figures |pdf=|usr=}}{{33290420}} gab.com Text On the authenticity of COVID-19 case figures JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33290420 #FOAvip In this article, we study the applicability of Benford's law and Zipf's law to national COVID-19 case figures with the aim of establishing guidelines upon which methods of fraud detection in epidemiology, based on formal statistical analysis, can be developed. Moreover, these approaches may also be used in evaluating the performance of public health surveillance systems. We provide theoretical arguments for why the empirical laws should hold in the early stages of an epidemic, along with preliminary empirical evidence in support of these claims. Based on data published by the World Health Organization and various national governments, we find empirical evidence that suggests that both Benford's law and Zipf's law largely hold across countries, and deviations can be readily explained. To the best of our knowledge, this paper is among the first to present a practical application of Zipf's law to fraud detection. Twit Text FOAVip On the authenticity of COVID-19 case figures ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33290420 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33290420 #FOAvip English Wikipedia <ref>{{Cite pmid|33290420}}</ref> On the authenticity of COVID-19 case figures #MMPMID33290420 Kennedy AP; Yam SCP PLoS One 2020[]; 15 (12): e0243123 PMID33290420show ga In this article, we study the applicability of Benford's law and Zipf's law to national COVID-19 case figures with the aim of establishing guidelines upon which methods of fraud detection in epidemiology, based on formal statistical analysis, can be developed. Moreover, these approaches may also be used in evaluating the performance of public health surveillance systems. We provide theoretical arguments for why the empirical laws should hold in the early stages of an epidemic, along with preliminary empirical evidence in support of these claims. Based on data published by the World Health Organization and various national governments, we find empirical evidence that suggests that both Benford's law and Zipf's law largely hold across countries, and deviations can be readily explained. To the best of our knowledge, this paper is among the first to present a practical application of Zipf's law to fraud detection. |COVID-19/*epidemiology[MESH] |Humans[MESH] |Models, Theoretical[MESH] |Pandemics/*statistics & numerical data[MESH] |Reproducibility of Results[MESH]On the authenticity of COVID-19 case figures (epmc).pdf DeepDyve Pubget Overpricing