What you saw was what you got? Correcting reported incidence data for testing intensity.

Authors

Rasmus Kristoffer Pedersen, Christian Berrig, Tamás Tekeli, Gergely Röst, Viggo Andreasen

Published in

Epidemics. Volume 57. Pages 100949. Oct 08, 2026. Epub Oct 08, 2026.

Abstract

During the COVID-19 pandemic, non-pharmaceutical interventions were employed to limit the spread of the SARS-CoV-2 virus. In some countries, this included large-scale voluntary testing of non-symptomatic individuals to gauge the prevalence in the general population and to identify asymptomatic and pre-symptomatic infections in order to reduce disease spread. Differences between the intensity of testing means that comparing case-counts across borders may be deceptive. In this work, we use mathematical modeling to investigate the dynamics of both observed and unobserved infections as a function of the rate of voluntary testing. The model indicates that increasing the rate of testing causes the number of observed cases to increase, despite a decrease in prevalence due to testing. For large testing rates, the observed case-counts also decrease. The non-monotonicity of the observed cases explains some discrepancies seen when comparing uncorrected case-counts between countries. One such example was observed during the winter 2020/2021 COVID-19 epidemics in Denmark and Hungary, where reported case-counts were comparable, despite differences in prevalence. We determine a quantitative measure for the ascertainment ratio between observed cases and all infections, allowing for test-intensity correction of case-counts. This measure allows us to correct case-counts for testing intensity during the winter 2021/2022 epidemic of the Omicron variant (BA.1 and BA.2) in Denmark, showing good agreement with results from serology-studies. While the model does not capture the full complexity of epidemic dynamics, it provides a simple way to correct case-counts for differences in testing, simplifying comparison across international borders and testing activities.

PMID:
42887302
Bibliographic data and abstract were imported from PubMed on 17 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.