Diagnostic Strategies for Endemic Coronavirus Disease 2019 (COVID-19) Rapid Antigen Tests, Repeated Testing, and Prevalence Boundaries

Abstract

Context.—Coronavirus disease 2019 (COVID-19) rapid antigen tests generate intrinsically fast, inherently spatial, and immediately actionable results. They quickly confirm COVID-19, but weakly rule out infection. Test performance depends on prevalence and testing protocol. Both affect predictive values. Objectives.—To use original mathematics and visual logistics for interpreting COVID-19 rapid antigen test performance patterns, gauge the influence of prevalence, and evaluate repeated testing. Design.—Mathematica and open access software helped graph relationships, perform recursive computations, and compare performance patterns. PubMed retrieved articles addressing endemic COVID-19 were reviewed. Results.—Tiered sensitivity/specificity comprise the following: T1 (90%/95%), T2 (95%/97.5%), and T3 (100%/ ≥99%). Performance of self-tests and home antigen tests with US Food and Drug Administration Emergency Use Authorization peaks in low prevalence. Fall-off in performance appears with increasing prevalence because suboptimal sensitivity creates false negatives. The rate of false omissions limits clinical use because of prevalence boundaries based on tolerance for risk. Mathematical analysis supports testing twice to improve predictive values and extend prevalence boundaries nearly to levels of herd immunity. Conclusions.—COVID-19 is quickly becoming endemic. Suboptimal sensitivity of rapid antigen tests limits performance in high prevalence. Risk of contagion in packed spaces (eg, airplanes) might be avoided with dual testing 36 hours apart, allowing time for viral load to increase. Awareness of community prevalence and proof of improved performance with repeated testing will help manage COVID-19 risk, while meeting rapid decision-making needs for highly contagious and new variants (eg, Delta). New COVID-19 variants call for high-quality, low cost, readily accessible, fast, user friendly, and ubiquitous point-of-care testing.