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Bayesian imputation of COVID‐19 positive test counts for nowcasting under reporting lag.

Authors :
Jersakova, Radka
Lomax, James
Hetherington, James
Lehmann, Brieuc
Nicholson, George
Briers, Mark
Holmes, Chris
Source :
Journal of the Royal Statistical Society: Series C (Applied Statistics); Aug2022, Vol. 71 Issue 4, p834-860, 27p
Publication Year :
2022

Abstract

Obtaining up to date information on the number of UK COVID‐19 regional infections is hampered by the reporting lag in positive test results for people with COVID‐19 symptoms. In the UK, for 'Pillar 2' swab tests for those showing symptoms, it can take up to five days for results to be collated. We make use of the stability of the under reporting process over time to motivate a statistical temporal model that infers the final total count given the partial count information as it arrives. We adopt a Bayesian approach that provides for subjective priors on parameters and a hierarchical structure for an underlying latent intensity process for the infection counts. This results in a smoothed time‐series representation nowcasting the expected number of daily counts of positive tests with uncertainty bands that can be used to aid decision making. Inference is performed using sequential Monte Carlo. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
COVID-19 testing
COVID-19

Details

Language :
English
ISSN :
00359254
Volume :
71
Issue :
4
Database :
Complementary Index
Journal :
Journal of the Royal Statistical Society: Series C (Applied Statistics)
Publication Type :
Academic Journal
Accession number :
158572152
Full Text :
https://doi.org/10.1111/rssc.12557