id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-348061-ssjl2w7l	Chamberlain, Samuel D	Real-time detection of COVID-19 epicenters within the United States using a network of smart thermometers	2020-04-10		.txt	text/plain	2745	164	52	Leveraging data from a geospatial network of thermometers encompassing more than one million users across the US, we identify anomalies by generating accurate, county-specific forecasts of seasonal ILI from a point prior to a potential outbreak and comparing real-time data to these expectations. is the (which was not peer-reviewed) The copyright holder for this preprint Here, we outline a method to identify illness incidence anomalies using a geospatial network of smart thermometers, where county-scale anomalies are flagged in real-time. Our anomaly detection method follows three core steps: 1) Generate county-specific forecasts of influenza-like illness (ILI) from a time point prior to a potential outbreak, 2) compare real-time thermometer-derived ILI to forecast expectations when new data is aggregated daily, and 3) flag anomalous ILI values by evaluating the probability that the current signal is driven by regular seasonal influenza.	./cache/cord-348061-ssjl2w7l.txt	./txt/cord-348061-ssjl2w7l.txt