id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-297013-0ykz2raz	Agarwal, D. K.	Alternative Approaches for Modelling COVID-19:High-Accuracy Low-Data Predictions	2020-07-25		.txt	text/plain	4308	256	61	Methods: Instead of relying on highly parameterized models, we design and train multiple neural networks with data on a national and state level, from 9 COVID-19 affected countries, including Indian and US states and territories. Further, we use an array of curve-fitting techniques on government-reported numbers of COVID-19 infections and deaths, separately projecting and collating curves from multiple regions across the globe, at multiple levels of granularity, combining heavily-localized extrapolations to create accurate national predictions. Further, we use an array of curve-fitting techniques on government-reported numbers of COVID-19 infections and deaths, separately projecting and collating curves from multiple regions across the globe, at multiple levels of granularity, combining heavily-localized extrapolations to create accurate national predictions. Therefore, we use curve-fitting and machine-learning models on national-and state-level data to predict government-reported numbers of total infections in multiple countries.	./cache/cord-297013-0ykz2raz.txt	./txt/cord-297013-0ykz2raz.txt