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10.1093/inthealth/ihab031 http://scihub22266oqcxt.onion/10.1093/inthealth/ihab031 34091670!8194983!34091670     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34091670&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 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+Health 2021 ; 13 (5): 410-420 Nephropedia Template TP {{tp|p=34091670|t=2021. Short-term forecasting of the COVID-19 outbreak in India |pdf=|usr=}}{{34091670}} gab.com Text Short-term forecasting of the COVID-19 outbreak in India JO: Int Health http://www.kidney.de/pget.php?&tw=1&pmid=34091670 #FOAvip As the outbreak of coronavirus disease 2019 (COVID-19) is rapidly spreading in different parts of India, a reliable forecast for the cumulative confirmed cases and the number of deaths can be helpful for policymakers in making the decisions for utilizing available resources in the country. Recently, various mathematical models have been used to predict the outbreak of COVID-19 worldwide and also in India. In this article we use exponential, logistic, Gompertz growth and autoregressive integrated moving average (ARIMA) models to predict the spread of COVID-19 in India after the announcement of various unlock phases. The mean absolute percentage error and root mean square error comparative measures were used to check the goodness-of-fit of the growth models and Akaike information criterion for ARIMA model selection. Using COVID-19 pandemic data up to 20 December 2020 from India and its five most affected states (Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu and Kerala), we report 15-days-ahead forecasts for cumulative confirmed cases and the number of deaths. Based on available data, we found that the ARIMA model is the best-fitting model for COVID-19 cases in India and its most affected states. Twit Text FOAVip Short-term forecasting of the COVID-19 outbreak in India ????Int Health http://www.kidney.de/pget.php?&tw=1&pmid=34091670 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34091670 #FOAvip English Wikipedia <ref>{{Cite pmid|34091670}}</ref> Short-term forecasting of the COVID-19 outbreak in India #MMPMID34091670 Mangla S; Pathak AK; Arshad M; Haque U Int Health 2021[Sep]; 13 (5): 410-420 PMID34091670show ga As the outbreak of coronavirus disease 2019 (COVID-19) is rapidly spreading in different parts of India, a reliable forecast for the cumulative confirmed cases and the number of deaths can be helpful for policymakers in making the decisions for utilizing available resources in the country. Recently, various mathematical models have been used to predict the outbreak of COVID-19 worldwide and also in India. In this article we use exponential, logistic, Gompertz growth and autoregressive integrated moving average (ARIMA) models to predict the spread of COVID-19 in India after the announcement of various unlock phases. The mean absolute percentage error and root mean square error comparative measures were used to check the goodness-of-fit of the growth models and Akaike information criterion for ARIMA model selection. Using COVID-19 pandemic data up to 20 December 2020 from India and its five most affected states (Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu and Kerala), we report 15-days-ahead forecasts for cumulative confirmed cases and the number of deaths. Based on available data, we found that the ARIMA model is the best-fitting model for COVID-19 cases in India and its most affected states. |*COVID-19[MESH] |*Pandemics[MESH] |Disease Outbreaks[MESH] |Humans[MESH] |India/epidemiology[MESH] |Models, Statistical[MESH]Short-term forecasting of the COVID-19 outbreak in India (epmc).pdf DeepDyve Pubget Overpricing