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

Jersakova, Radka; Lomax, James; Hetherington, James; Lehmann, Brieuc; Nicholson, George; Briers, Mark; Holmes, Chris; (2022) Bayesian imputation of COVID-19 positive test counts for nowcasting under reporting lag. Journal of the Royal Statistical Society: Series C (Applied Statistics) , 71 (4) pp. 834-860. 10.1111/rssc.12557. Green open access

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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.

Type: Article
Title: Bayesian imputation of COVID-19 positive test counts for nowcasting under reporting lag
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/rssc.12557
Publisher version: https://doi.org/10.1111/rssc.12557
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords: Science & Technology, Physical Sciences, Statistics & Probability, Mathematics, Bayesian inference, COVID-19, nowcasting, reporting lag, sequential Monte Carlo, uncertainty quantification, MODELS
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
URI: https://discovery.ucl.ac.uk/id/eprint/10148354
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