id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-346185-qmu1mrmx	Velásquez, Ricardo Manuel Arias	Forecast and evaluation of COVID-19 spreading in USA with Reduced-space Gaussian process regression	2020-05-22		.txt	text/plain	1122	80	58	title: Forecast and evaluation of COVID-19 spreading in USA with Reduced-space Gaussian process regression In this report, we analyze historical and forecast infections for COVID-19 death based on Reduced-Space Gaussian Process Regression associated to chaotic Dynamical Systems with information obtained in 82 days with continuous learning, day by day, from January 21(th), 2020 to April 12(th). According last results, COVID-19 could be predicted with Gaussian models mean-field models can be meaningfully used to gather a quantitative picture of the epidemic spreading, with infections, fatality and recovery rate. able on the Center for Systems Science and Engineering at Johns Hopkins University [6] , the available data analyzed is considered between January 21 th 2020 and April 39 12 th 2020, included, with a feedback process in a neural network applied; it allows 40 to examined the information in real time in each state, at Fig. 1 • .	./cache/cord-346185-qmu1mrmx.txt	./txt/cord-346185-qmu1mrmx.txt