Quantifying previous SARS-CoV-2 infection through mixture modelling of antibody levels
Christian Bottomley, Mark Otiende, Sophie Uyoga, Katherine E. Gallagher, E. Wangeci Kagucia, Anthony Etyang, Daisy Mugo, John N. Gitonga, Henry Karanja, James Nyagwange, Ifedayo Adetifa, Ambrose Agweyu, D. James Nokes, George M. Warimwe, J. Anthony G. Scott
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.