id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-302056-wvf6cpib	Benatia, D.	Estimating COVID-19 Prevalence in the United States: A Sample Selection Model Approach	2020-04-30		.txt	text/plain	5016	315	52	Background: Public health efforts to determine population infection rates from coronavirus disease 2019 (COVID-19) have been hampered by limitations in testing capabilities and the large shares of mild and asymptomatic cases. Public health efforts to determine population infection rates from coronavirus disease 2019 (COVID -19) have been hampered by limitations in testing capabilities and the large shares of mild and asymptomatic cases. We developed a methodology that corrects observed positive test rates for non-random sampling to estimate population infection rates across U.S. states from March 31 to April 7. We developed a methodology that corrects observed positive test rates for non-random sampling to estimate population infection rates across U.S. states from March 31 to April 7. Because the severity of sample selection bias depends on the extent of testing, these disparities create large uncertainty regarding the relative disease prevalence across jurisdictions, and may contribute to the wide differences in estimated case fatality rates [10, 11] .	./cache/cord-302056-wvf6cpib.txt	./txt/cord-302056-wvf6cpib.txt