id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-219107-klpmipaj	Zachreson, Cameron	Risk mapping for COVID-19 outbreaks using mobility data	2020-08-14		.txt	text/plain	5901	261	45	For community transmission scenarios, our results demonstrate that mobility data adds the most value to risk predictions when case counts are low and spatially clustered. In each case, we use the Facebook mobility data that was available during the early stages of the outbreak to estimate future spatial patterns of relative transmission risk. For each of the three outbreak scenarios, we present the mobility-based estimates of the relative transmission risk distribution, and a time-varying correlation between our estimate and the case numbers ascertained through contact tracing and testing programs. Our results indicate that aggregate mobility data can be a useful tool in estimation of COVID-19 transmission risk diffusion from locations where active cases have been identified. A heat map (Supplemental Figure S1 ) of the average number of Facebook users present during the nighttime period (2am to 10am) as a proportion of the estimated resident population reported by the ABS (2018 [32] ) shows qualitative similarity to the spatial distributions of active cases and relative risk shown in Figure 5 	./cache/cord-219107-klpmipaj.txt	./txt/cord-219107-klpmipaj.txt