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Real-time estimation of COVID-19 infections: deconvolution and sensor fusion. (English) Zbl 07535200

Summary: 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.

MSC:

62-XX Statistics

Software:

GitHub; EpiEstim
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Full Text: DOI arXiv

References:

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