id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
cord-328069-a9fi9ssg	Pathan, Refat Khan	Time Series Prediction of COVID-19 by Mutation Rate Analysis using Recurrent Neural Network-based LSTM Model	2020-06-13		.txt	text/plain	3407	167	61	About 0.1% increment in mutation rate is found for mutating of nucleotides from T to C and G, C to G and G to T. While a decrement of 0.1% is seen for mutating of T to A, and A to C. It is found that this model can be used to predict day basis mutation rates if more patient data is available in updated time. Accurate determination of mutation rates is essential to comprehend the evolution of this virus and to determine the risk of emergent infectious disease.	cache/cord-328069-a9fi9ssg.txt	txt/cord-328069-a9fi9ssg.txt
