id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-303660-2bxpqhro	Direkoglu, C.	Worldwide and Regional Forecasting of Coronavirus (Covid-19) Spread using a Deep Learning Model	2020-05-26		.txt	text/plain	3985	251	62	We design a deep neural network, which consist of Long Short Term Memory (LSTM) layer, dropout layer, and fully connected layers, to analyze the reported Covid-19 cases and predict the possible future scenarios for the spread in China, Europe, Middle East and worldwide. Forecasting cumulative total number of Covid-19 cases worldwide using a model with RMSE of 39699 predicts that outbreak size may reach to 2,600,000 within the next 10 days and continue to grow linearly. . https://doi.org/10.1101/2020.05.23.20111039 doi: medRxiv preprint Figure 8 : Forecasting cumulative total number of deaths from Covid-19 worldwide using a model with RMSE of 5657.1; predicts that the death toll may increase to 100,000 within the next 10 days. We design a deep learning model to forecast the spread of the novel coronavirus, Covid-19, in China, Europe, Middle East region and worldwide.	./cache/cord-303660-2bxpqhro.txt	./txt/cord-303660-2bxpqhro.txt