id	author	title	date	pages	extension	mime	words	sentences	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	4359	202	53	This study uses real-world location-based service data collected from anonymized mobile devices to uncover mobility changes during COVID-19 and under the 'Stay-at-home' state orders in the U.S. The study measures human mobility with two important metrics: daily average number of trips per person and daily average person-miles traveled. While the data confirmed that, nationwide, mobility had dropped significantly one week or even two weeks before the orders were issued, an additional 6.1% decrease in daily average number of trips per person and 10.8% decrease in daily average person-miles traveled (PMT) were observed in the week after the order took effect across different states. To quantify how people in different states responded to "Stay-at-home" orders during the COVID-19 pandemic, we studied the longitudinal changes in state-level mobility using a generalized additive model (GAM) (Wood, 2017; Hastie, 1993; Hastie & Tibshirani, 1990 ) of daily average number of trips per person and daily average person-miles traveled.	./cache/cord-125190-87wcp92x.txt	./txt/cord-125190-87wcp92x.txt