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10.1016/j.dib.2020.105340 http://scihub22266oqcxt.onion/10.1016/j.dib.2020.105340 32181302!7063124!32181302     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32181302&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       Data+Brief 2020 ; 29 (?): 105340 Nephropedia Template TP {{tp|p=32181302|t=2020. Application of the ARIMA model on the COVID-2019 epidemic dataset |pdf=|usr=}}{{32181302}} gab.com Text Application of the ARIMA model on the COVID-2019 epidemic dataset JO: Data Brief http://www.kidney.de/pget.php?&tw=1&pmid=32181302 #FOAvip Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time. Twit Text FOAVip Application of the ARIMA model on the COVID-2019 epidemic dataset ????Data Brief http://www.kidney.de/pget.php?&tw=1&pmid=32181302 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32181302 #FOAvip English Wikipedia <ref>{{Cite pmid|32181302}}</ref> Application of the ARIMA model on the COVID-2019 epidemic dataset #MMPMID32181302 Benvenuto D; Giovanetti M; Vassallo L; Angeletti S; Ciccozzi M Data Brief 2020[Apr]; 29 (?): 105340 PMID32181302show ga Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time. ?Application of the ARIMA model on the COVID-2019 epidemic dataset (epmc).pdf DeepDyve Pubget Overpricing