Imperial College London
Alternate

DrLucyOkell

Faculty of Medicine, School of Public Health

Senior Lecturer & Royal Society Dorothy Hodgkin Fellow
 
 
 
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Contact

 

l.okell Website

 
 
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Location

 

410School of Public HealthWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Okell:2022:10.1038/s43856-022-00106-7,
author = {Okell, L and Brazeau, NF and Verity, R and Jenks, S and Fu, H and Whittaker, C and Winskill, P and Dorigatti, I and Walker, P and Riley, S and Schnekenberg, RP and Hoeltgebaum, H and Mellan, TA and Mishra, S and Unwin, H and Watson, O and Cucunuba, Z and Baguelin, M and Whittles, L and Bhatt, S and Ghani, A and Ferguson, N},
doi = {10.1038/s43856-022-00106-7},
journal = {Communications Medicine},
pages = {1--13},
title = {Estimating the COVID-19 infection fatality ratio accounting for seroreversion using statistical modelling},
url = {http://dx.doi.org/10.1038/s43856-022-00106-7},
volume = {2},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Background: The infection fatality ratio (IFR) is a key statistic for estimating the burden of coronavirus disease 2019 (COVID-19) and has been continuously debated throughout the COVID-19 pandemic. The age-specific IFR can be quantified using antibody surveys to estimate total infections, but requires consideration of delay-distributions from time from infection to seroconversion, time to death, and time to seroreversion (i.e. antibody waning) alongside serologic test sensitivity and specificity. Previous IFR estimates have not fully propagated uncertainty or accounted for these potential biases, particularly seroreversion. Methods: We built a Bayesian statistical model that incorporates these factors and applied this model to simulated data and 10 serologic studies from different countries.  Results: We demonstrate that seroreversion becomes a crucial factor as time accrues but is less important during first-wave, short-term dynamics. We additionally show that disaggregating surveys by regions with higher versus lower disease burden can inform serologic test specificity estimates. The overall IFR in each setting was estimated at 0.49 -2.53%.Conclusion: We developed a robust statistical framework to account for full uncertainties in the parameters determining IFR. We provide code for others to apply these methods to further datasets and future epidemics.
AU  - Okell,L
AU  - Brazeau,NF
AU  - Verity,R
AU  - Jenks,S
AU  - Fu,H
AU  - Whittaker,C
AU  - Winskill,P
AU  - Dorigatti,I
AU  - Walker,P
AU  - Riley,S
AU  - Schnekenberg,RP
AU  - Hoeltgebaum,H
AU  - Mellan,TA
AU  - Mishra,S
AU  - Unwin,H
AU  - Watson,O
AU  - Cucunuba,Z
AU  - Baguelin,M
AU  - Whittles,L
AU  - Bhatt,S
AU  - Ghani,A
AU  - Ferguson,N
DO  - 10.1038/s43856-022-00106-7
EP  - 13
PY  - 2022///
SN  - 2730-664X
SP  - 1
TI  - Estimating the COVID-19 infection fatality ratio accounting for seroreversion using statistical modelling
T2  - Communications Medicine
UR  - http://dx.doi.org/10.1038/s43856-022-00106-7
UR  - https://www.nature.com/articles/s43856-022-00106-7
UR  - http://hdl.handle.net/10044/1/96618
VL  - 2
ER  -
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