Quantifying previous SARS-CoV-2 infection through mixture modelling of antibody levels.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
26 10 2021
26 10 2021
Historique:
received:
23
04
2021
accepted:
17
09
2021
entrez:
27
10
2021
pubmed:
28
10
2021
medline:
5
11
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 34702829
doi: 10.1038/s41467-021-26452-z
pii: 10.1038/s41467-021-26452-z
pmc: PMC8548402
doi:
Substances chimiques
Antibodies, Viral
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
6196Subventions
Organisme : Foreign and Commonwealth Office (FCO)
ID : EARF/ITT/039
Organisme : Wellcome Trust (Wellcome)
ID : 220991/Z/20/Z
Organisme : Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation)
ID : INV-017547
Organisme : RCUK | Medical Research Council (MRC)
ID : MR/R010161/1
Organisme : Wellcome Trust
ID : 214320
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Wellcome Trust (Wellcome)
ID : 203077/Z/16/Z
Informations de copyright
© 2021. The Author(s).
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