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.
@article{arxiv.2112.06697,
title = {Real-Time Estimation of COVID-19 Infections: Deconvolution and Sensor Fusion},
author = {Maria Jahja and Andrew Chin and Ryan J. Tibshirani},
journal= {arXiv preprint arXiv:2112.06697},
year = {2022}
}