Real-Time Estimation of COVID-19 Infections: Deconvolution and Sensor Fusion
Statistical Science
; 37(2):207, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1862209
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.
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
Statistical Science
Year:
2022
Document Type:
Article
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