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