id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
cord-125190-87wcp92x	Xiong, Chenfeng	Data-Driven Modeling Reveals the Impact of Stay-at-Home Orders on Human Mobility during the COVID-19 Pandemic in the U.S	2020-05-02		.txt	text/plain	4362	156	47	2) Interactions between stay-at-home order and state, to capture the varying effect of policies across different states; 3) Time-varying patterns, including an average changing pattern and a seasonally changing pattern (weekly patterns), to fit the autoregressive time series; 4) Spatiotemporal interactions, to capture the spatiotemporal heterogeneity over time across different states. We reorganized the mobility data and anchored the data to the dates when different states announced their Stay-at-home orders, and then quantified the human mobility changes, compared to the January benchmark, three weeks, two weeks, and one week before and after the orders were issued (illustrated in Figure 2 ).	cache/cord-125190-87wcp92x.txt	txt/cord-125190-87wcp92x.txt
