id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-104486-syirijql	Adiga, Aniruddha	Data-driven modeling for different stages of pandemic response	2020-09-21		.txt	text/plain	7175	346	44	Governments have been forced to respond to the rapidly changing dynamics of the pandemic, and are becoming increasingly reliant on different modeling and analytical techniques to understand, forecast, plan and respond; this includes statistical methods and decision support methods using multi-agent models, such as: (i) forecasting epidemic outcomes (e.g., case counts, mortality and hospital demands), using a diverse set of data-driven methods e.g., ARIMA type time series forecasting, Bayesian techniques and deep learning, e.g., [1] [2] [3] [4] [5] , (ii) disease surveillance, e.g., [6, 7] , and (iii) counter-factual analysis of epidemics using multi-agent models, e.g., [8] [9] [10] [11] [12] [13] ; indeed, the results of [11, 14] were very influential in the early decisions for lockdowns in a number of countries.	./cache/cord-104486-syirijql.txt	./txt/cord-104486-syirijql.txt