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We propose, implement, and evaluate a method to estimate the daily number ofnew symptomatic COVID-19 infections, at the level of individual U.S. counties,by deconvolving daily reported COVID-19 case counts using an estimatedsymptom-onset-to-case-report delay distribution. Importantly, we focus onestimating infections in real-time (rather than retrospectively), which posesnumerous challenges. To address these, we develop new methodology for both thedistribution estimation and deconvolution steps, and we employ a sensor fusionlayer (which fuses together predictions from models that are trained to trackinfections based on auxiliary surveillance streams) in order to improveaccuracy and stability.

Real-Time Estimation of COVID-19 Infections: Deconvolution and Sensor Fusion