Imperial College London
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ProfessorHelenWard

Faculty of Medicine, School of Public Health

Professor of Public Health
 
 
 
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Contact

 

+44 (0)20 7594 3303h.ward Website

 
 
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Location

 

311School of Public HealthWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Eales:2022:10.1016/j.epidem.2022.100604,
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 = {10.1016/j.epidem.2022.100604},
journal = {Epidemics: the journal of infectious disease dynamics},
title = {Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number},
url = {http://dx.doi.org/10.1016/j.epidem.2022.100604},
volume = {40},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The time-varying reproduction number () 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  from case data. However, these are not easily adapted to point prevalence data nor can they infer  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  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  over shorter periods tracking a temporary increase above one during late-May 2020, a gradual increase in  over the summer of 2020 as restrictions were eased, and a reduction in  during England’s second national lockdown followed by an increase as the Alpha variant surged. The model is robust against both under-fitting and over-fitting and is able to interpolate between periods of available data; it is a particularly versatile model when growth rate can change over small timescales, as in the current SARS-CoV-2 pandemic. This work highlights the importance of pairing robust methods with representative samples to track pandemics.
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  - 10.1016/j.epidem.2022.100604
PY  - 2022///
SN  - 1755-4365
TI  - Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number
T2  - Epidemics: the journal of infectious disease dynamics
UR  - http://dx.doi.org/10.1016/j.epidem.2022.100604
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000827602400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.sciencedirect.com/science/article/pii/S1755436522000482?via%3Dihub
UR  - http://hdl.handle.net/10044/1/99415
VL  - 40
ER  -
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