Correcting prevalence estimation for biased sampling with testing errors

Abstract

Surveillance studies for Covid-19 prevalence estimation are subject to sampling bias due to oversampling of symptomatic individuals and error-prone tests, particularly rapid antigen tests which are known to have high false negative rates for asymptomatic individuals. This results in naïve estimators which can be very far from the truth.In this work, we present a method that removes these two sources of error directly. Moreover, our procedure can be easily extended to the stratified error situation in which a test has very different error rate profiles for symptomatic and asymptomatic individuals as is the case for rapid antigen testing. The result is an easily understandable four-step algorithm that produces much more reliable prevalence estimates as demonstrated on data from the Israeli Ministry of Health. Thus it may re-open the debate about whether we are under-valuing rapid testing as a surveillance tool and may have policy implications in Third-World countries or disadvantaged communities where access to PCR testing may be less accessible.

“Back to the future” projections for COVID-19 surges

“…Analyzing past estimators of prevalence is somewhat more standard [1][2][3]. However, forecasting is a more daunting task.…”