Evaluating the Usefulness of Population-Wide COVID-19 Testing in the Omicron Era: Insights from a German Model

Abstract

Background: The Omicron variant of SARS-CoV-2, which has become dominant worldwide since late 2021, presents a unique challenge due to its high rate of asymptomatic transmission. This study evaluates the efficacy and value of population-wide testing, including self-testing, in the context of COVID-19, particularly under the Omicron variant, using data from Germany.

Methods: A decision-analytical model and secondary data was used for assessing the impact of systematic screening and testing for COVID-19. Various scenarios were taken into consideration including seasonal patterns of COVID-19 transmission and the potential for annual waves. The model assessed the clinical benefits of testing against the backdrop of vaccine effectiveness, transmission rates, and the potential to prevent severe clinical events, including death, ICU admission, and long COVID syndrome.

Results: The study found that the value of mass testing and self-testing for private use was highly contingent on the transmission rate and the scenario of COVID-19 waves (seasonal vs. continuous). For winter waves, a very high incidence rate was required to justify testing, while for continuous waves, testing could be valuable for those in contact with individuals in their last decade of life. The analysis highlighted the limitations of mass testing when community transmission rates were low and the potential value of testing in high-risk contacts or amidst new outbreaks.

Conclusion: The findings suggest that the resumption of testing during winter waves is unlikely to provide significant clinical benefits given the current understanding of Omicron's transmission and immunity waning. This study underscores the need for a nuanced approach to COVID-19 testing policies, considering both the epidemiological context and the practical implications of testing strategies.

Fig. 1

Relationship between 7-day incidence per 100,000 inhabitants and testing years needed to prevent one clinical event.

Fig. 2

Tornado diagram demonstrating the results of the one-way sensitivity analysis. The variables are ordered by the break-even 7-day incidence rate. IFR, infection fatality rate; ICU, intensive care unit.