id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-353976-gns5omyb	Kafieh, Rahele	COVID-19 in Iran: A Deeper Look Into The Future	2020-04-27		.txt	text/plain	4523	261	57	. https://doi.org/10.1101/2020.04.24.20078477 doi: medRxiv preprint analysis of the accuracy of our forecasting model, and some of the possible future trends for COVID-19 situation in Iran and other countries. used phenomenological models that have been validated during previous outbreaks to generate and assess short-term forecasts of the cumulative number of confirmed reported cases in Hubei province, the epicenter of the epidemic, and for the overall trajectory in China, excluding the province of Hubei. In [14] , Liu and colleagues used early reported case data and built a model to predict the cumulative number of cases for the COVID-19 epidemic in China. The machine learning models are trained and tested based on 18576, 18576, and 17569 occurrences of daily number of confirmed, death, and recovered COVID-19 cases. Figure 7 is designed to show MAPE value for predicting occurrences of confirmed, death, and recovered cases from COVID-19 when lags of 1-20 days are used on validation data in preparatory model to find the optimum lag.	./cache/cord-353976-gns5omyb.txt	./txt/cord-353976-gns5omyb.txt