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10.1017/S0950268820001909 http://scihub22266oqcxt.onion/10.1017/S0950268820001909 32843111!7477461!32843111     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Epidemiol+Infect 2020 ; 148 (ä): e192 Nephropedia Template TP {{tp|p=32843111|t=2020. Application of records theory on the COVID-19 pandemic in Lebanon: prediction and prevention |pdf=|usr=}}{{32843111}} gab.com Text Application of records theory on the COVID-19 pandemic in Lebanon: prediction and prevention JO: Epidemiol Infect http://www.kidney.de/pget.php?&tw=1&pmid=32843111 #FOAvip Given the fast spread of the novel coronavirus (COVID-19) worldwide and its classification by the World Health Organization (WHO) as being one of the worst pandemics in history, several scientific studies are carried out using various statistical and mathematical models to predict and study the likely evolution of this pandemic in the world. In the present research paper, we present a brief study aiming to predict the probability of reaching a new record number of COVID-19 cases in Lebanon, based on the record theory, giving more insights about the rate of its quick spread in Lebanon. The main advantage of the records theory resides in avoiding several statistical constraints concerning the choice of the underlying distribution and the quality of the residuals. In addition, this theory could be used, in cases where the number of available observations is somehow small. Moreover, this theory offers an alternative solution in case where machine learning techniques and long-term memory models are inapplicable because they need a considerable amount of data to be performant. The originality of this paper lies in presenting a new statistical approach allowing the early detection of unexpected phenomena such as the new pandemic COVID-19. For this purpose, we used epidemiological data from Johns Hopkins University to predict the trend of COVID-2019 in Lebanon. Our method is useful in calculating the probability of reaching a new record as well as studying the propagation of the disease. It also computes the probabilities of the waiting time to observe the future COVID-19 record. Our results obviously confirm the quick spread of the disease in Lebanon over a short time. Twit Text FOAVip Application of records theory on the COVID-19 pandemic in Lebanon: prediction and prevention ????Epidemiol Infect http://www.kidney.de/pget.php?&tw=1&pmid=32843111 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32843111 #FOAvip English Wikipedia <ref>{{Cite pmid|32843111}}</ref> Application of records theory on the COVID-19 pandemic in Lebanon: prediction and prevention #MMPMID32843111 Khraibani Z; Khraibani J; Kobeissi M; Atoui A Epidemiol Infect 2020[Aug]; 148 (ä): e192 PMID32843111show ga Given the fast spread of the novel coronavirus (COVID-19) worldwide and its classification by the World Health Organization (WHO) as being one of the worst pandemics in history, several scientific studies are carried out using various statistical and mathematical models to predict and study the likely evolution of this pandemic in the world. In the present research paper, we present a brief study aiming to predict the probability of reaching a new record number of COVID-19 cases in Lebanon, based on the record theory, giving more insights about the rate of its quick spread in Lebanon. The main advantage of the records theory resides in avoiding several statistical constraints concerning the choice of the underlying distribution and the quality of the residuals. In addition, this theory could be used, in cases where the number of available observations is somehow small. Moreover, this theory offers an alternative solution in case where machine learning techniques and long-term memory models are inapplicable because they need a considerable amount of data to be performant. The originality of this paper lies in presenting a new statistical approach allowing the early detection of unexpected phenomena such as the new pandemic COVID-19. For this purpose, we used epidemiological data from Johns Hopkins University to predict the trend of COVID-2019 in Lebanon. Our method is useful in calculating the probability of reaching a new record as well as studying the propagation of the disease. It also computes the probabilities of the waiting time to observe the future COVID-19 record. Our results obviously confirm the quick spread of the disease in Lebanon over a short time. |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology/transmission[MESH] |Data Interpretation, Statistical[MESH] |Humans[MESH] |Lebanon/epidemiology[MESH] |Models, Statistical[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology/transmission[MESH]Application of records theory on the COVID-19 pandemic in Lebanon: pre(epmc).pdf DeepDyve Pubget Overpricing