id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-312911-nqq87d0m	Zou, D.	Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States	2020-05-25		.txt	text/plain	5032	283	62	We propose a new epidemic model (SuEIR) for forecasting the spread of COVID-19, including numbers of confirmed and fatality cases at national and state levels in the United States. Specifically, the SuEIR model is a variant of the SEIR model by taking into account the untested/unreported cases of COVID-19, and trained by machine learning algorithms based on the reported historical data. Besides providing basic projections for confirmed and fatality cases, the proposed SuEIR model is also able to predict the peak date of active cases, and estimate the basic reproduction number (R0). Based on the proposed model, we are able to make accurate predictions on the numbers of confirmed cases and fatality cases for nation, states and and counties. Moreover, our model can also predict the peak dates of active cases and estimate the basic reproduction number (R 0 ) of different states in the US.	./cache/cord-312911-nqq87d0m.txt	./txt/cord-312911-nqq87d0m.txt