Imperial College London
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DrChristinaAtchison

Faculty of Medicine, School of Public Health

Principal Clinical Academic Fellow
 
 
 
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Contact

 

christina.atchison11

 
 
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Location

 

Reynolds BuildingCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@article{Eales:2022:10.1101/2022.02.04.22270426,
author = {Eales, O and Ainslie, KEC and Walters, CE and Wang, H and Atchison, C and Ashby, D and Donnelly, CA and Cooke, G and Barclay, W and Ward, H and Darzi, A and Elliott, P and Riley, S},
doi = {10.1101/2022.02.04.22270426},
title = {Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number},
url = {http://dx.doi.org/10.1101/2022.02.04.22270426},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>Abstract</jats:title><jats:p>The time-varying reproduction number (<jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats:bold><jats:italic>t</jats:italic></jats:bold></jats:sub>) can change rapidly over the course of a pandemic due to changing restrictions, behaviours, and levels of population immunity. Many methods exist that allow the estimation of <jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats:bold><jats:italic>t</jats:italic></jats:bold></jats:sub> from case data. However, these are not easily adapted to point prevalence data nor can they infer <jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats:bold><jats:italic>t</jats:italic></jats:bold></jats:sub> across periods of missing data. We developed a Bayesian P-spline model suitable for fitting to a wide range of epidemic time-series, including point-prevalence data. We demonstrate the utility of the model by fitting to periodic daily SARS-CoV-2 swab-positivity data in England from the first 7 rounds (May 2020 – December 2020) of the REal-time Assessment of Community Transmission-1 (REACT-1) study. Estimates of <jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats:bold><jats:italic>t</jats:italic></jats:bold></jats:sub> over the period of two subsequent rounds (6-8 weeks) and single rounds (2-3 weeks) inferred using the Bayesian P-spline model were broadly consistent with estimates from a simple exponential model, with overlapping credible intervals. However, there were sometimes substantial differences in point estimates. The Bayesian P-spline model was further able to infer changes in <jats:bold><jats:italic>R</jats:italic></jats:bold><jats:sub><jats
AU  - Eales,O
AU  - Ainslie,KEC
AU  - Walters,CE
AU  - Wang,H
AU  - Atchison,C
AU  - Ashby,D
AU  - Donnelly,CA
AU  - Cooke,G
AU  - Barclay,W
AU  - Ward,H
AU  - Darzi,A
AU  - Elliott,P
AU  - Riley,S
DO  - 10.1101/2022.02.04.22270426
PY  - 2022///
TI  - Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number
UR  - http://dx.doi.org/10.1101/2022.02.04.22270426
ER  -
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