id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-125295-p7q9t1se	Burghardt, Keith	Unequal Impact and Spatial Aggregation Distort COVID-19 Growth Rates	2020-04-27		.txt	text/plain	2698	139	58	Using confirmed infections and deaths data from a variety of sources around U.S. and the world, we show that the impact of COVID-19 is highly unequal, with hot spots emerging at multiple spatial scales (3): from individual facilities (4) and city neighborhoods (5) , to U.S. counties and states (6) , to nations (7) . To better understand aggregation bias, we create a simple stochastic model that is variant of a Reed-Hughes mechanism (8), with synthetic communities in which the disease arrives at different times and grows at different rates. The size of the outbreak is highly correlated with the growth rate in the subregion; therefore, when the synthetic data is aggregated to simulate state or national statistics, these hot spots systematically amplify the estimated growth rates, much like what is observed empirically. These hot spots bias aggregated growth rates COVID-19 statistics, making the disease appear to grow faster at a larger scale than it does within the constituent communities.	./cache/cord-125295-p7q9t1se.txt	./txt/cord-125295-p7q9t1se.txt