Real-time detection of COVID-19 epicenters within the United States using a network of smart thermometers
Chamberlain, S. D.; Singh, I.; Ariza, C. A.; Daitch, A. L.; Philips, P. B.; Dalziel, B. D.
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Containing outbreaks of infectious disease requires rapid identification of transmission hotspots, as the COVID-19 pandemic demonstrates. Focusing limited public health resources on transmission hotspots can contain spread, thus reducing morbidity and mortality, but rapid data on community-level disease dynamics is often unavailable. Here, we demonstrate an approach to identify anomalously elevated levels of influenza-like illness (ILI) in real-time, at the scale of US counties. 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. Anomalies are strongly correlated with COVID-19 case counts and may provide an early-warning system to locate outbreak epicenters. One Sentence SummaryDistributed networks of smart thermometers track COVID-19 transmission epicenters in real-time.
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