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

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.

• Publication year

  2021

• Venue

  Statistical Science

• Publication date

  2021-12-13

• Fields of study

  Medicine, Computer Science, Mathematics

• Identifiers
  DOI 10.1214/22-sts856arXiv 2112.06697
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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