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arXiv · 2112.06697

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

Maria Jahja·Andrew Chin·Ryan J. Tibshirani
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Abstract

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.

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Maria Jahja, Andrew Chin, Ryan J. Tibshirani. 2021-12-13. Real-Time Estimation of COVID-19 Infections: Deconvolution and Sensor Fusion. https://arxiv.org/abs/2112.06697

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