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Quantifying previous SARS-CoV-2 infection through mixture modelling of antibody levels
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Bottomley, C., Otiende, M., Uyoga, S., Gallagher, K., Kagucia, E. W., Etyang, A. O., Mugo, D., Gitonga, J., Karanja, H., Nyagwange, J., Adetifa, I. M. O., Agweyu, A., Nokes, D. J., Warimwe, G. M. and Scott, J. A. G. (2021) Quantifying previous SARS-CoV-2 infection through mixture modelling of antibody levels. Nature Communications, 12 . 6196. doi:10.1038/s41467-021-26452-z ISSN 2041-1723.
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Official URL: https://doi.org/10.1038/s41467-021-26452-z
Abstract
As countries decide on vaccination strategies and how to ease movement restrictions, estimating the proportion of the population previously infected with SARS-CoV-2 is important for predicting the future burden of COVID-19. This proportion is usually estimated from serosurvey data in two steps: first the proportion above a threshold antibody level is calculated, then the crude estimate is adjusted using external estimates of sensitivity and specificity. A drawback of this approach is that the PCR-confirmed cases used to estimate the sensitivity of the threshold may not be representative of cases in the wider population—e.g. they may be more recently infected and more severely symptomatic. Mixture modelling offers an alternative approach that does not require external data from PCR-confirmed cases. Here we illustrate the bias in the standard threshold-based approach by comparing both approaches using data from several Kenyan serosurveys. We show that the mixture model analysis produces estimates of previous infection that are often substantially higher than the standard threshold analysis.
Item Type: | Journal Article | |||||||||||||||||||||||||||
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Subjects: | Q Science > QR Microbiology R Medicine > RA Public aspects of medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) | |||||||||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | COVID-19 (Disease) , COVID-19 (Disease) -- Epidemiology , COVID-19 (Disease) -- Prevention, COVID-19 disease -- Patients -- Care, COVID-19 (Disease) -- Patients -- Treatment , COVID-19 (Disease) -- Vaccination, Immunoglobulins -- Mathematical models, Coronavirus infections | |||||||||||||||||||||||||||
Journal or Publication Title: | Nature Communications | |||||||||||||||||||||||||||
Publisher: | Nature Publishing Group | |||||||||||||||||||||||||||
ISSN: | 2041-1723 | |||||||||||||||||||||||||||
Official Date: | 26 October 2021 | |||||||||||||||||||||||||||
Dates: |
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Volume: | 12 | |||||||||||||||||||||||||||
Article Number: | 6196 | |||||||||||||||||||||||||||
DOI: | 10.1038/s41467-021-26452-z | |||||||||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||||||||||||||
Date of first compliant deposit: | 13 October 2021 | |||||||||||||||||||||||||||
Date of first compliant Open Access: | 13 October 2021 | |||||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
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