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Estimating the COVID-19 infection fatality ratio accounting for seroreversion using statistical modelling

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Title: Estimating the COVID-19 infection fatality ratio accounting for seroreversion using statistical modelling
Authors: Okell, L
Brazeau, NF
Verity, R
Jenks, S
Fu, H
Whittaker, C
Winskill, P
Dorigatti, I
Walker, P
Riley, S
Schnekenberg, RP
Hoeltgebaum, H
Mellan, TA
Mishra, S
Unwin, H
Watson, O
Cucunuba, Z
Baguelin, M
Whittles, L
Bhatt, S
Ghani, A
Ferguson, N
Item Type: Journal Article
Abstract: 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.
Issue Date: 19-May-2022
Date of Acceptance: 22-Mar-2022
URI: http://hdl.handle.net/10044/1/96618
DOI: 10.1038/s43856-022-00106-7
ISSN: 2730-664X
Publisher: Nature
Start Page: 1
End Page: 13
Journal / Book Title: Communications Medicine
Volume: 2
Issue: 54
Copyright Statement: © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Sponsor/Funder: The Royal Society
Medical Research Council (MRC)
Wellcome Trust
Funder's Grant Number: DH140134
Keywords: Computational biology and bioinformatics
Respiratory tract diseases
Publication Status: Published
Online Publication Date: 2022-05-19
Appears in Collections:Department of Infectious Diseases
Faculty of Medicine
Imperial College London COVID-19
School of Public Health

This item is licensed under a Creative Commons License Creative Commons