id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-314211-tv1nhojk	Eltoukhy, Abdelrahman E. E.	Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations	2020-09-27		.txt	text/plain	9260	551	57	The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. [37] have proposed an AI-based algorithm for predicting COVID-19 cases using a hybrid Recurrent Neural Network (RNN) with a Long Short-Term Memory (LSTM) model. These important factors include population, median age index, public and private healthcare expenditure, air quality as a CO 2 trend, seasonality as month of data collection, number of arrivals in the country/territory, and education index. First, there is no previous study that simultaneously considers the historical data of the number of COVID-19 cases and most of the external factors that affect the spread of the virus. These external factors include population, median age index, public and private healthcare expenditure, air quality as a CO 2 trend, seasonality as month of data collection, number of arrivals in the country/territory, and education index.	./cache/cord-314211-tv1nhojk.txt	./txt/cord-314211-tv1nhojk.txt