id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-309333-lvcp8imi	Fenichel, Eli P	A cell phone data driven time use analysis of the COVID-19 epidemic	2020-04-23		.txt	text/plain	5386	301	56	Here we build on prior epidemiological time use modeling (Bayham and Fenichel, 2016; Bayham et al., 2015; Berry et al., 2018) to adapt the common SEIR framework to a dynamic time use structure that enables differential behavior by health status in order to incorporate smartphone tracking data into a model of the COVID-19 epidemic for every county in the United States. In prior research, we developed an economic-epidemiological model based on a time-varying conditional proportional mixing structure (Fenichel, 2013; Fenichel et al., 2011) that enables physical distancing behavior to vary based on health state and respond to the state of the epidemic. Serological tests capable of identifying recovered and immune individuals (which are not yet available) are important, and the greatest benefits are in counties where getting recovered individuals back to baseline schedules reduces the greatest share of cases ( Figure 6 ) coupled with those counties likely to experience the greatest hardships from infection (Maher et al., 2020) .	./cache/cord-309333-lvcp8imi.txt	./txt/cord-309333-lvcp8imi.txt