id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-351818-znv51wx9	Ge, Q.	Recurrent Neural Reinforcement Learning for Counterfactual Evaluation of Public Health Interventions on the Spread of Covid-19 in the world	2020-07-10		.txt	text/plain	4941	380	54	Therefore, we formulated real-time forecasting and evaluation of multiple public health intervention problems into off-policy evaluation (OPE) and counterfactual outcome forecasting problems and integrated RL and recurrent neural network (RNN) for exploring public health intervention strategies to slow down the spread of Covid-19 worldwide, given the historical data that may have been generated by different public health intervention policies. Widely used statistical and computer methods for modeling of Covid-19 simulate the transmission dynamics of epidemics to understand their underlying mechanisms, forecast the trajectory of epidemics, and assess the potential impact of a number of public health measures on curbing the spread speed of Covid-19 [2] [3] [4] [5] [6] [7] [8] . . https://doi.org/10.1101/2020.07.08.20149146 doi: medRxiv preprint learn the optimal control (intervention) policy, we need to identify the system underlying the dynamics of Covid-19. We propose to use RNN-based counterfactual action evaluation as a general framework for modeling and forecasting the spread of Covid-19 over time with multiple interventions [30] .	./cache/cord-351818-znv51wx9.txt	./txt/cord-351818-znv51wx9.txt