id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-295853-lxakf79k	Kumar, Pavan	Forecasting the dynamics of COVID-19 Pandemic in Top 15 countries in April 2020: ARIMA Model with Machine Learning Approach	2020-03-31		.txt	text/plain	1719	112	54	title: Forecasting the dynamics of COVID-19 Pandemic in Top 15 countries in April 2020: ARIMA Model with Machine Learning Approach We used the data of cumulative confirmed death and recovery of COVID-19 cases reported from January 21 until March 26, 2020, that were obtained from John Hopkins Coronavirus resource center (https://coronavirus.jhu.edu/). We analyzed the data using dynamic models to generate 30 days forecasts and to understand the positive effect in the near future as well as projecting trends over trajectories. https://doi.org/10.1101/2020.03.30.20046227 doi: medRxiv preprint cumulative incident cases, mortality, and recovery of COVID-19 information among the top 15 affected countries is shown in Figure 2 . Top countries' data of China, Italy, Spain, and Iran showed highly disastrous mortality and badly effected with a vast number of COVID-19 cases. https://doi.org/10.1101/2020.03.30.20046227 doi: medRxiv preprint Fig. 2 : Comparisons between cumulative reported, recovery and death incidence of cases with COVID-19 on the top 15 affected countries .	./cache/cord-295853-lxakf79k.txt	./txt/cord-295853-lxakf79k.txt