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10.1007/s40808-020-00890-y http://scihub22266oqcxt.onion/10.1007/s40808-020-00890-y 32838022!7363688!32838022     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Model+Earth+Syst+Environ 2021 ; 7 (2): 1385-1391 Nephropedia Template TP {{tp|p=32838022|t=2021. Spatial prediction of COVID-19 epidemic using ARIMA techniques in India |pdf=|usr=}}{{32838022}} gab.com Text Spatial prediction of COVID-19 epidemic using ARIMA techniques in India JO: Model Earth Syst Environ http://www.kidney.de/pget.php?&tw=1&pmid=32838022 #FOAvip The latest Coronavirus (COVID-19) has become an infectious disease that causes millions of people to infect. Effective short-term prediction models are designed to estimate the number of possible events. The data obtained from 30th January to 26 April, 2020 and from 27th April 2020 to 11th May 2020 as modelling and forecasting samples, respectively. Spatial distribution of disease risk analysis is carried out using weighted overlay analysis in GIS platform. The epidemiologic pattern in the prevalence and incidence of COVID-2019 is forecasted with the Autoregressive Integrated Moving Average (ARIMA). We assessed cumulative confirmation cases COVID-19 in Indian states with a high daily incidence in the task of time-series forecasting. Such efficiency metrics such as an index of increasing results, mean absolute error (MAE), and a root mean square error (RMSE) are the out-of-samples for the prediction precision of model. Results shows west and south of Indian district are highly vulnerable for COVID-2019. The accuracy of ARIMA models in forecasting future epidemic of COVID-2019 proved the effectiveness in epidemiological surveillance. For more in-depth studies, our analysis may serve as a guide for understanding risk attitudes and social media interactions across countries. Twit Text FOAVip Spatial prediction of COVID-19 epidemic using ARIMA techniques in India ????Model Earth Syst Environ http://www.kidney.de/pget.php?&tw=1&pmid=32838022 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32838022 #FOAvip English Wikipedia <ref>{{Cite pmid|32838022}}</ref> Spatial prediction of COVID-19 epidemic using ARIMA techniques in India #MMPMID32838022 Roy S; Bhunia GS; Shit PK Model Earth Syst Environ 2021[]; 7 (2): 1385-1391 PMID32838022show ga The latest Coronavirus (COVID-19) has become an infectious disease that causes millions of people to infect. Effective short-term prediction models are designed to estimate the number of possible events. The data obtained from 30th January to 26 April, 2020 and from 27th April 2020 to 11th May 2020 as modelling and forecasting samples, respectively. Spatial distribution of disease risk analysis is carried out using weighted overlay analysis in GIS platform. The epidemiologic pattern in the prevalence and incidence of COVID-2019 is forecasted with the Autoregressive Integrated Moving Average (ARIMA). We assessed cumulative confirmation cases COVID-19 in Indian states with a high daily incidence in the task of time-series forecasting. Such efficiency metrics such as an index of increasing results, mean absolute error (MAE), and a root mean square error (RMSE) are the out-of-samples for the prediction precision of model. Results shows west and south of Indian district are highly vulnerable for COVID-2019. The accuracy of ARIMA models in forecasting future epidemic of COVID-2019 proved the effectiveness in epidemiological surveillance. For more in-depth studies, our analysis may serve as a guide for understanding risk attitudes and social media interactions across countries. äSpatial prediction of COVID-19 epidemic using ARIMA techniques in Indi(epmc).pdf DeepDyve Pubget Overpricing